{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 0.34269118309020996 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 0.0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d85ce3160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 0.7666378021240234 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 1.0} is: \n",
      "[[ 10.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  16.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  10. ...  22.  22.  24.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 130. 130. 122.]\n",
      " [ 20.  20.  22. ... 130. 130. 122.]\n",
      " [ 20.  20.  24. ... 122. 122. 154.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83be6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 1.2073416709899902 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 2.0} is: \n",
      "[[ 15.   4.   0. ...   0.   0.   0.]\n",
      " [  4.  24.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  15. ...   1.   1.   5.]\n",
      " ...\n",
      " [  0.   0.   1. ...  87.  58.  23.]\n",
      " [  0.   0.   1. ...  58.  67.  23.]\n",
      " [  0.   0.   5. ...  23.  23. 101.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83be9f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 1.911818504333496 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 3.0} is: \n",
      "[[ 20.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  32.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  20. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 116.   8.   0.]\n",
      " [  0.   0.   0. ...   8.  60.   0.]\n",
      " [  0.   0.   0. ...   0.   0.  50.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83b755f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 2.1614458560943604 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 4.0} is: \n",
      "[[ 25.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  40.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  25. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 145.   0.   0.]\n",
      " [  0.   0.   0. ...   0.  75.   0.]\n",
      " [  0.   0.   0. ...   0.   0.  55.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83b72978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 2.8392231464385986 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 5.0} is: \n",
      "[[ 30.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  48.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  30. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 174.   0.   0.]\n",
      " [  0.   0.   0. ...   0.  90.   0.]\n",
      " [  0.   0.   0. ...   0.   0.  66.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83ae3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 3.197157859802246 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 6.0} is: \n",
      "[[ 35.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  56.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  35. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 203.   0.   0.]\n",
      " [  0.   0.   0. ...   0. 105.   0.]\n",
      " [  0.   0.   0. ...   0.   0.  77.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83adc320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 3.8528859615325928 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 7.0} is: \n",
      "[[ 40.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  64.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  40. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 232.   0.   0.]\n",
      " [  0.   0.   0. ...   0. 120.   0.]\n",
      " [  0.   0.   0. ...   0.   0.  88.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83ae63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 3.7338240146636963 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 8.0} is: \n",
      "[[ 45.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  72.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  45. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 261.   0.   0.]\n",
      " [  0.   0.   0. ...   0. 135.   0.]\n",
      " [  0.   0.   0. ...   0.   0.  99.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83b6ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 4.214876890182495 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 9.0} is: \n",
      "[[ 50.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  80.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  50. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 290.   0.   0.]\n",
      " [  0.   0.   0. ...   0. 150.   0.]\n",
      " [  0.   0.   0. ...   0.   0. 110.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d82275470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 4.636343955993652 seconds ---\n",
      "\n",
      "gram matrix with parameters {'height': 10.0} is: \n",
      "[[ 55.   0.   0. ...   0.   0.   0.]\n",
      " [  0.  88.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  55. ...   0.   0.   0.]\n",
      " ...\n",
      " [  0.   0.   0. ... 319.   0.   0.]\n",
      " [  0.   0.   0. ...   0. 165.   0.]\n",
      " [  0.   0.   0. ...   0.   0. 121.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d83b743c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 3/13530 [00:00<09:29, 23.74it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                            \n",
      "4. Getting final performances...\n",
      "\n",
      "best_params_out:  [{'height': 1.0}]\n",
      "best_params_in:  [{'alpha': 0.1}]\n",
      "\n",
      "best_val_perf:  7.408897274451954\n",
      "best_val_std:  0.7280307805221202\n",
      "final_performance:  7.6770690731806726\n",
      "final_confidence:  2.3181322643015494\n",
      "train_performance:  5.909130825932086\n",
      "train_std:  0.24106718316563544\n",
      "\n",
      "time to calculate gram matrix with different hyperpapams: 2.62±1.45\n",
      "time to calculate best gram matrix:  0.7666378021240234 s\n",
      "\n",
      "params                                 train_perf           valid_perf              test_perf                   gram_matrix_time\n",
      "-------------------------------------  -------------------  ----------------------  ------------------------  ------------------\n",
      "{'alpha': '1.00e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '1.00e-02', 'height': 1.0}   5.89±0.25            7.64±0.81               8.03±2.47                               0.77\n",
      "{'alpha': '1.00e-02', 'height': 2.0}   1850.53±8495.35      7413.48±27850.20        49953.89±265798.39                      1.21\n",
      "{'alpha': '1.00e-02', 'height': 3.0}   158730.24±717452.81  1437804.88±4133498.55   7667000.67±30318904.22                  1.91\n",
      "{'alpha': '1.00e-02', 'height': 4.0}   16653.08±46197.89    7904151.09±13562096.05  12222217.46±42270377.03                 2.16\n",
      "{'alpha': '1.00e-02', 'height': 5.0}   94.41±3.53           413264.50±53384.79      368929.20±230070.59                     2.84\n",
      "{'alpha': '1.00e-02', 'height': 6.0}   93.57±3.14           486696.80±61273.57      466831.43±311986.09                     3.2\n",
      "{'alpha': '1.00e-02', 'height': 7.0}   94.69±3.31           559652.14±70435.33      475694.36±284948.70                     3.85\n",
      "{'alpha': '1.00e-02', 'height': 8.0}   93.48±4.49           611340.81±68548.76      700494.13±365043.03                     3.73\n",
      "{'alpha': '1.00e-02', 'height': 9.0}   94.71±3.79           690040.31±78333.14      654124.04±481253.77                     4.21\n",
      "{'alpha': '1.00e-02', 'height': 10.0}  94.38±3.55           744311.87±94286.93      693669.90±420716.47                     4.64\n",
      "{'alpha': '1.26e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '1.26e-02', 'height': 1.0}   5.88±0.25            7.63±0.81               8.01±2.47                               0.77\n",
      "{'alpha': '1.26e-02', 'height': 2.0}   457.04±894.89        2764.93±4001.17         3439.97±8149.06                         1.21\n",
      "{'alpha': '1.26e-02', 'height': 3.0}   16399.32±44893.28    486473.91±878785.82     2532452.26±12164366.72                  1.91\n",
      "{'alpha': '1.26e-02', 'height': 4.0}   10055.95±20739.19    4953114.74±7792697.43   10859850.17±34085132.33                 2.16\n",
      "{'alpha': '1.26e-02', 'height': 5.0}   94.41±3.53           328261.40±42402.42      293043.28±182747.20                     2.84\n",
      "{'alpha': '1.26e-02', 'height': 6.0}   93.57±3.14           386590.29±48669.86      370811.67±247811.84                     3.2\n",
      "{'alpha': '1.26e-02', 'height': 7.0}   94.69±3.31           444541.08±55947.74      377849.83±226338.89                     3.85\n",
      "{'alpha': '1.26e-02', 'height': 8.0}   93.47±4.49           485598.83±54448.67      556413.39±289959.99                     3.73\n",
      "{'alpha': '1.26e-02', 'height': 9.0}   94.71±3.79           548112.48±62221.03      519585.83±382262.80                     4.21\n",
      "{'alpha': '1.26e-02', 'height': 10.0}  94.38±3.55           591221.82±74893.73      550994.12±334182.43                     4.64\n",
      "{'alpha': '1.58e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '1.58e-02', 'height': 1.0}   5.88±0.25            7.61±0.81               7.99±2.46                               0.77\n",
      "{'alpha': '1.58e-02', 'height': 2.0}   359.40±684.20        2142.02±3083.64         4610.21±16154.28                        1.21\n",
      "{'alpha': '1.58e-02', 'height': 3.0}   22326.13±48176.67    437770.33±670209.83     20347881.66±108491010.11                1.91\n",
      "{'alpha': '1.58e-02', 'height': 4.0}   12497.56±41451.95    3028725.85±4555456.36   54058567.27±275562982.64                2.16\n",
      "{'alpha': '1.58e-02', 'height': 5.0}   94.40±3.53           260741.05±33678.82      232764.95±145156.90                     2.84\n",
      "{'alpha': '1.58e-02', 'height': 6.0}   93.56±3.14           307072.88±38658.38      294540.47±196836.41                     3.2\n",
      "{'alpha': '1.58e-02', 'height': 7.0}   94.68±3.31           353105.11±44439.84      300129.16±179783.46                     3.85\n",
      "{'alpha': '1.58e-02', 'height': 8.0}   93.47±4.49           385718.42±43248.57      441965.99±230319.41                     3.73\n",
      "{'alpha': '1.58e-02', 'height': 9.0}   94.71±3.79           435375.20±49422.73      412718.34±303631.48                     4.21\n",
      "{'alpha': '1.58e-02', 'height': 10.0}  94.38±3.55           469618.08±59489.16      437662.73±265446.00                     4.64\n",
      "{'alpha': '2.00e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '2.00e-02', 'height': 1.0}   5.87±0.25            7.59±0.80               7.96±2.45                               0.77\n",
      "{'alpha': '2.00e-02', 'height': 2.0}   1180.92±5490.13      2807.18±7564.94         3211.03±8110.46                         1.21\n",
      "{'alpha': '2.00e-02', 'height': 3.0}   19476.61±42072.93    500334.14±1350251.05    1143078.49±4131816.59                   1.91\n",
      "{'alpha': '2.00e-02', 'height': 4.0}   4176.04±6369.35      1976787.50±2773660.48   4584635.41±14409870.89                  2.16\n",
      "{'alpha': '2.00e-02', 'height': 5.0}   94.39±3.53           207107.73±26749.42      184884.17±115297.86                     2.84\n",
      "{'alpha': '2.00e-02', 'height': 6.0}   93.56±3.14           243909.95±30705.97      233956.10±156345.19                     3.2\n",
      "{'alpha': '2.00e-02', 'height': 7.0}   94.68±3.31           280474.95±35298.79      238393.44±142803.17                     3.85\n",
      "{'alpha': '2.00e-02', 'height': 8.0}   93.47±4.49           306380.60±34352.02      351057.19±182945.21                     3.73\n",
      "{'alpha': '2.00e-02', 'height': 9.0}   94.70±3.79           345824.80±39256.67      327830.47±241172.40                     4.21\n",
      "{'alpha': '2.00e-02', 'height': 10.0}  94.38±3.55           373024.79±47252.87      347640.41±210846.71                     4.64\n",
      "{'alpha': '2.51e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '2.51e-02', 'height': 1.0}   5.87±0.25            7.57±0.80               7.93±2.44                               0.77\n",
      "{'alpha': '2.51e-02', 'height': 2.0}   121.83±158.01        858.18±574.96           911.94±1169.07                          1.21\n",
      "{'alpha': '2.51e-02', 'height': 3.0}   11751.37±18519.69    494373.74±1338223.62    702338.74±2004156.41                    1.91\n",
      "{'alpha': '2.51e-02', 'height': 4.0}   4819.54±13751.61     1513719.19±2711713.85   1886618.16±5353975.45                   2.16\n",
      "{'alpha': '2.51e-02', 'height': 5.0}   94.39±3.53           164505.27±21245.20      146851.12±91579.98                      2.84\n",
      "{'alpha': '2.51e-02', 'height': 6.0}   93.55±3.14           193737.86±24389.15      185832.24±124181.87                     3.2\n",
      "{'alpha': '2.51e-02', 'height': 7.0}   94.67±3.30           222782.76±28037.79      189355.02±113428.68                     3.85\n",
      "{'alpha': '2.51e-02', 'height': 8.0}   93.46±4.49           243360.33±27285.24      278845.76±145314.55                     3.73\n",
      "{'alpha': '2.51e-02', 'height': 9.0}   94.70±3.79           274692.39±31181.49      260401.64±191559.40                     4.21\n",
      "{'alpha': '2.51e-02', 'height': 10.0}  94.37±3.55           296298.02±37533.24      276133.14±167476.95                     4.64\n",
      "{'alpha': '3.16e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '3.16e-02', 'height': 1.0}   5.86±0.25            7.54±0.79               7.90±2.43                               0.77\n",
      "{'alpha': '3.16e-02', 'height': 2.0}   119.76±131.64        1144.70±1075.34         1217.35±2791.34                         1.21\n",
      "{'alpha': '3.16e-02', 'height': 3.0}   23870.45±49916.46    686824.04±1699826.57    1269093.59±4089024.38                   1.91\n",
      "{'alpha': '3.16e-02', 'height': 4.0}   1928.81±2359.42      727382.03±998246.18     1121543.70±3310129.29                   2.16\n",
      "{'alpha': '3.16e-02', 'height': 5.0}   94.38±3.53           130664.94±16873.05      116640.41±72740.19                      2.84\n",
      "{'alpha': '3.16e-02', 'height': 6.0}   93.54±3.14           153884.75±19371.53      147606.09±98633.64                      3.2\n",
      "{'alpha': '3.16e-02', 'height': 7.0}   94.67±3.30           176956.23±22270.18      150402.42±90095.69                      3.85\n",
      "{'alpha': '3.16e-02', 'height': 8.0}   93.45±4.49           193301.55±21671.89      221486.20±115423.45                     3.73\n",
      "{'alpha': '3.16e-02', 'height': 9.0}   94.69±3.79           218189.91±24767.14      206841.02±152150.38                     4.21\n",
      "{'alpha': '3.16e-02', 'height': 10.0}  94.37±3.55           235351.78±29812.67      219332.90±133027.13                     4.64\n",
      "{'alpha': '3.98e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '3.98e-02', 'height': 1.0}   5.85±0.25            7.51±0.78               7.86±2.42                               0.77\n",
      "{'alpha': '3.98e-02', 'height': 2.0}   79.49±67.45          803.01±819.16           690.36±815.07                           1.21\n",
      "{'alpha': '3.98e-02', 'height': 3.0}   74400.21±351437.51   982172.51±3670953.64    677551.00±1839635.51                    1.91\n",
      "{'alpha': '3.98e-02', 'height': 4.0}   1377.61±1529.42      453881.11±607348.87     708319.75±2069204.34                    2.16\n",
      "{'alpha': '3.98e-02', 'height': 5.0}   94.36±3.53           103784.62±13400.12      92643.19±57775.22                       2.84\n",
      "{'alpha': '3.98e-02', 'height': 6.0}   93.53±3.14           122228.31±15385.88      117242.00±78339.96                      3.2\n",
      "{'alpha': '3.98e-02', 'height': 7.0}   94.66±3.30           140554.93±17688.80      119461.27±71561.64                      3.85\n",
      "{'alpha': '3.98e-02', 'height': 8.0}   93.45±4.49           153538.46±17213.06      175923.88±91680.10                      3.73\n",
      "{'alpha': '3.98e-02', 'height': 9.0}   94.68±3.79           173308.40±19672.05      164296.31±120846.69                     4.21\n",
      "{'alpha': '3.98e-02', 'height': 10.0}  94.36±3.55           186940.47±23680.00      174214.87±105662.66                     4.64\n",
      "{'alpha': '5.01e-02', 'height': 0.0}   16.86±0.30           16.98±0.32              16.87±2.47                              0.34\n",
      "{'alpha': '5.01e-02', 'height': 1.0}   5.85±0.25            7.48±0.77               7.81±2.40                               0.77\n",
      "{'alpha': '5.01e-02', 'height': 2.0}   175.72±407.99        1367.95±2528.49         1554.43±3674.17                         1.21\n",
      "{'alpha': '5.01e-02', 'height': 3.0}   10213.37±13634.83    184237.07±258652.98     250319.51±461469.15                     1.91\n",
      "{'alpha': '5.01e-02', 'height': 4.0}   1044.38±1092.46      291295.25±374352.33     453376.67±1297552.43                    2.16\n",
      "{'alpha': '5.01e-02', 'height': 5.0}   94.35±3.53           82432.83±10641.48       73581.54±45888.11                       2.84\n",
      "{'alpha': '5.01e-02', 'height': 6.0}   93.52±3.13           97082.71±12219.97       93122.95±62220.11                       3.2\n",
      "{'alpha': '5.01e-02', 'height': 7.0}   94.65±3.30           111640.35±14049.68      94883.86±56839.51                       3.85\n",
      "{'alpha': '5.01e-02', 'height': 8.0}   93.44±4.49           121953.52±13671.28      139732.44±72820.09                      3.73\n",
      "{'alpha': '5.01e-02', 'height': 9.0}   94.68±3.79           137657.75±15624.87      130501.86±95981.29                      4.21\n",
      "{'alpha': '5.01e-02', 'height': 10.0}  94.35±3.55           148486.00±18808.65      138376.36±83926.29                      4.64\n",
      "{'alpha': '6.31e-02', 'height': 0.0}   16.86±0.30           16.98±0.33              16.87±2.47                              0.34\n",
      "{'alpha': '6.31e-02', 'height': 1.0}   5.86±0.25            7.45±0.76               7.76±2.37                               0.77\n",
      "{'alpha': '6.31e-02', 'height': 2.0}   110.93±213.95        831.64±1083.91          596.38±746.01                           1.21\n",
      "{'alpha': '6.31e-02', 'height': 3.0}   6282.14±8477.48      116882.78±167776.26     228186.40±612608.49                     1.91\n",
      "{'alpha': '6.31e-02', 'height': 4.0}   813.63±812.77        190407.96±231916.14     293339.87±814604.18                     2.16\n",
      "{'alpha': '6.31e-02', 'height': 5.0}   94.33±3.53           65472.51±8450.21        58440.34±36445.85                       2.84\n",
      "{'alpha': '6.31e-02', 'height': 6.0}   93.51±3.13           77108.86±9705.20        73964.51±49415.66                       3.2\n",
      "{'alpha': '6.31e-02', 'height': 7.0}   94.63±3.30           88672.70±11159.03       75361.33±45145.31                       3.85\n",
      "{'alpha': '6.31e-02', 'height': 8.0}   93.42±4.49           96864.71±10857.94       110984.57±57839.05                      3.73\n",
      "{'alpha': '6.31e-02', 'height': 9.0}   94.66±3.79           109339.44±12410.08      103657.97±76230.00                      4.21\n",
      "{'alpha': '6.31e-02', 'height': 10.0}  94.34±3.55           117940.54±14939.19      109908.82±66660.47                      4.64\n",
      "{'alpha': '7.94e-02', 'height': 0.0}   16.86±0.30           16.99±0.33              16.87±2.47                              0.34\n",
      "{'alpha': '7.94e-02', 'height': 1.0}   5.88±0.24            7.42±0.75               7.72±2.35                               0.77\n",
      "{'alpha': '7.94e-02', 'height': 2.0}   63.80±83.80          475.48±401.58           427.51±497.69                           1.21\n",
      "{'alpha': '7.94e-02', 'height': 3.0}   3925.00±3974.10      75713.01±97197.23       122070.67±312003.20                     1.91\n",
      "{'alpha': '7.94e-02', 'height': 4.0}   646.78±617.83        126949.82±143939.78     192176.69±511584.55                     2.16\n",
      "{'alpha': '7.94e-02', 'height': 5.0}   94.31±3.53           52000.47±6709.63        46413.27±28945.58                       2.84\n",
      "{'alpha': '7.94e-02', 'height': 6.0}   93.48±3.13           61243.08±7707.64        58746.44±39244.72                       3.2\n",
      "{'alpha': '7.94e-02', 'height': 7.0}   94.61±3.30           70428.85±8862.90        59854.05±35856.28                       3.85\n",
      "{'alpha': '7.94e-02', 'height': 8.0}   93.41±4.49           76935.98±8623.23        88149.34±45939.18                       3.73\n",
      "{'alpha': '7.94e-02', 'height': 9.0}   94.65±3.79           86845.42±9856.49        82335.12±60540.99                       4.21\n",
      "{'alpha': '7.94e-02', 'height': 10.0}  94.33±3.55           93677.42±11865.57       87296.25±52945.75                       4.64\n",
      "{'alpha': '1.00e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.87±2.47                              0.34\n",
      "{'alpha': '1.00e-01', 'height': 1.0}   5.91±0.24            7.41±0.73               7.68±2.32                               0.77\n",
      "{'alpha': '1.00e-01', 'height': 2.0}   126.94±278.94        693.99±922.66           889.86±1863.54                          1.21\n",
      "{'alpha': '1.00e-01', 'height': 3.0}   3929.85±4800.81      64825.58±88308.57       191137.08±526650.31                     1.91\n",
      "{'alpha': '1.00e-01', 'height': 4.0}   567.78±513.52        123813.55±220137.10     132054.41±320465.37                     2.16\n",
      "{'alpha': '1.00e-01', 'height': 5.0}   94.28±3.53           41299.26±5327.03        36859.86±22987.91                       2.84\n",
      "{'alpha': '1.00e-01', 'height': 6.0}   93.46±3.13           48640.46±6120.93        46658.31±31165.65                       3.2\n",
      "{'alpha': '1.00e-01', 'height': 7.0}   94.59±3.30           55937.26±7039.02        47536.19±28477.74                       3.85\n",
      "{'alpha': '1.00e-01', 'height': 8.0}   93.39±4.49           61106.03±6848.13        70010.68±36486.78                       3.73\n",
      "{'alpha': '1.00e-01', 'height': 9.0}   94.63±3.79           68977.79±7828.09        65397.79±48078.77                       4.21\n",
      "{'alpha': '1.00e-01', 'height': 10.0}  94.31±3.55           74404.55±9424.11        69334.47±42051.75                       4.64\n",
      "{'alpha': '1.26e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.87±2.47                              0.34\n",
      "{'alpha': '1.26e-01', 'height': 1.0}   5.97±0.24            7.41±0.71               7.65±2.28                               0.77\n",
      "{'alpha': '1.26e-01', 'height': 2.0}   71.34±76.07          654.66±789.97           792.38±1869.37                          1.21\n",
      "{'alpha': '1.26e-01', 'height': 3.0}   2292.71±3314.04      74814.93±244698.18      52157.76±94110.72                       1.91\n",
      "{'alpha': '1.26e-01', 'height': 4.0}   464.07±366.36        62269.14±55659.20       92898.49±203852.65                      2.16\n",
      "{'alpha': '1.26e-01', 'height': 5.0}   94.24±3.52           32799.01±4228.80        29271.34±18255.55                       2.84\n",
      "{'alpha': '1.26e-01', 'height': 6.0}   93.43±3.13           38629.86±4860.56        37056.39±24748.22                       3.2\n",
      "{'alpha': '1.26e-01', 'height': 7.0}   94.56±3.30           44426.20±5590.26        37751.79±22616.75                       3.85\n",
      "{'alpha': '1.26e-01', 'height': 8.0}   93.36±4.49           48531.87±5438.13        55602.64±28978.46                       3.73\n",
      "{'alpha': '1.26e-01', 'height': 9.0}   94.61±3.79           54785.05±6216.88        51943.99±38179.67                       4.21\n",
      "{'alpha': '1.26e-01', 'height': 10.0}  94.29±3.54           59095.58±7484.79        55066.93±33398.34                       4.64\n",
      "{'alpha': '1.58e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.87±2.47                              0.34\n",
      "{'alpha': '1.58e-01', 'height': 1.0}   6.06±0.23            7.44±0.68               7.64±2.25                               0.77\n",
      "{'alpha': '1.58e-01', 'height': 2.0}   55.82±67.03          548.63±712.40           645.23±1345.78                          1.21\n",
      "{'alpha': '1.58e-01', 'height': 3.0}   2037.75±3275.77      33900.84±45655.78       35449.52±49521.65                       1.91\n",
      "{'alpha': '1.58e-01', 'height': 4.0}   407.69±296.93        46011.33±34362.68       81695.67±164955.70                      2.16\n",
      "{'alpha': '1.58e-01', 'height': 5.0}   94.19±3.52           26047.04±3356.45        23243.60±14496.49                       2.84\n",
      "{'alpha': '1.58e-01', 'height': 6.0}   93.38±3.13           30678.19±3859.41        29429.34±19650.67                       3.2\n",
      "{'alpha': '1.58e-01', 'height': 7.0}   94.53±3.30           35282.66±4439.47        29979.79±17961.19                       3.85\n",
      "{'alpha': '1.58e-01', 'height': 8.0}   93.33±4.48           38543.88±4318.12        44157.95±23014.39                       3.73\n",
      "{'alpha': '1.58e-01', 'height': 9.0}   94.58±3.79           43511.36±4937.05        41257.28±30316.55                       4.21\n",
      "{'alpha': '1.58e-01', 'height': 10.0}  94.26±3.54           46935.24±5944.33        43733.84±26524.68                       4.64\n",
      "{'alpha': '2.00e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.87±2.47                              0.34\n",
      "{'alpha': '2.00e-01', 'height': 1.0}   6.20±0.23            7.51±0.64               7.67±2.21                               0.77\n",
      "{'alpha': '2.00e-01', 'height': 2.0}   116.25±231.80        1489.13±4978.40         1544.48±4377.89                         1.21\n",
      "{'alpha': '2.00e-01', 'height': 3.0}   1753.89±1667.24      52871.83±124646.98      32421.86±39697.35                       1.91\n",
      "{'alpha': '2.00e-01', 'height': 4.0}   304.60±242.60        30353.29±22361.81       39706.86±79913.75                       2.16\n",
      "{'alpha': '2.00e-01', 'height': 5.0}   94.13±3.52           20683.80±2663.52        18455.64±11510.56                       2.84\n",
      "{'alpha': '2.00e-01', 'height': 6.0}   93.33±3.13           24361.97±3064.16        23370.99±15601.53                       3.2\n",
      "{'alpha': '2.00e-01', 'height': 7.0}   94.48±3.30           28019.72±3525.36        23806.30±14263.15                       3.85\n",
      "{'alpha': '2.00e-01', 'height': 8.0}   93.29±4.48           30610.16±3428.47        35067.13±18276.95                       3.73\n",
      "{'alpha': '2.00e-01', 'height': 9.0}   94.54±3.78           34556.38±3920.45        32768.54±24070.64                       4.21\n",
      "{'alpha': '2.00e-01', 'height': 10.0}  94.23±3.54           37275.97±4720.70        34731.66±21064.74                       4.64\n",
      "{'alpha': '2.51e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.88±2.47                              0.34\n",
      "{'alpha': '2.51e-01', 'height': 1.0}   6.38±0.22            7.63±0.61               7.73±2.17                               0.77\n",
      "{'alpha': '2.51e-01', 'height': 2.0}   42.27±41.29          261.07±197.87           272.12±363.49                           1.21\n",
      "{'alpha': '2.51e-01', 'height': 3.0}   1613.98±2137.84      23979.41±35429.79       36075.94±89358.33                       1.91\n",
      "{'alpha': '2.51e-01', 'height': 4.0}   233.90±175.14        20402.13±13653.33       26084.86±50189.17                       2.16\n",
      "{'alpha': '2.51e-01', 'height': 5.0}   94.05±3.51           16423.67±2113.11        14652.48±9138.73                        2.84\n",
      "{'alpha': '2.51e-01', 'height': 6.0}   93.27±3.12           19344.87±2432.48        18558.72±12385.18                       3.2\n",
      "{'alpha': '2.51e-01', 'height': 7.0}   94.43±3.29           22250.59±2799.26        18902.55±11325.68                       3.85\n",
      "{'alpha': '2.51e-01', 'height': 8.0}   93.24±4.48           24308.22±2721.79        27846.06±14513.86                       3.73\n",
      "{'alpha': '2.51e-01', 'height': 9.0}   94.50±3.78           27443.21±3112.93        26025.72±19109.35                       4.21\n",
      "{'alpha': '2.51e-01', 'height': 10.0}  94.19±3.54           29603.35±3748.73        27581.01±16727.75                       4.64\n",
      "{'alpha': '3.16e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.88±2.47                              0.34\n",
      "{'alpha': '3.16e-01', 'height': 1.0}   6.62±0.21            7.81±0.57               7.85±2.14                               0.77\n",
      "{'alpha': '3.16e-01', 'height': 2.0}   30.38±30.44          148.71±90.02            169.66±234.91                           1.21\n",
      "{'alpha': '3.16e-01', 'height': 3.0}   1100.04±1315.87      14134.02±13044.86       15230.16±19952.37                       1.91\n",
      "{'alpha': '3.16e-01', 'height': 4.0}   194.97±134.68        14591.59±8534.11        17913.67±31634.69                       2.16\n",
      "{'alpha': '3.16e-01', 'height': 5.0}   93.96±3.51           13039.79±1675.90        11631.59±7254.70                        2.84\n",
      "{'alpha': '3.16e-01', 'height': 6.0}   93.19±3.12           15359.68±1930.71        14736.24±9830.34                        3.2\n",
      "{'alpha': '3.16e-01', 'height': 7.0}   94.35±3.29           17668.05±2222.50        15007.42±8992.36                        3.85\n",
      "{'alpha': '3.16e-01', 'height': 8.0}   93.18±4.47           19302.44±2160.46        22110.20±11524.72                       3.73\n",
      "{'alpha': '3.16e-01', 'height': 9.0}   94.44±3.78           21793.05±2471.49        20669.74±15168.46                       4.21\n",
      "{'alpha': '3.16e-01', 'height': 10.0}  94.14±3.54           23508.81±2976.67        21901.08±13282.74                       4.64\n",
      "{'alpha': '3.98e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.88±2.47                              0.34\n",
      "{'alpha': '3.98e-01', 'height': 1.0}   6.92±0.21            8.05±0.53               8.03±2.10                               0.77\n",
      "{'alpha': '3.98e-01', 'height': 2.0}   2747.21±14863.56     16409.93±89139.91       4249.78±22412.36                        1.21\n",
      "{'alpha': '3.98e-01', 'height': 3.0}   956.79±1100.86       12542.57±12507.92       10824.66±12603.34                       1.91\n",
      "{'alpha': '3.98e-01', 'height': 4.0}   166.60±102.93        10656.19±5342.84        12581.25±20009.41                       2.16\n",
      "{'alpha': '3.98e-01', 'height': 5.0}   93.84±3.50           10351.94±1328.63        9232.10±5758.14                         2.84\n",
      "{'alpha': '3.98e-01', 'height': 6.0}   93.09±3.11           12194.20±1532.14        11700.01±7800.95                        3.2\n",
      "{'alpha': '3.98e-01', 'height': 7.0}   94.26±3.28           14028.06±1764.36        11913.47±7138.93                        3.85\n",
      "{'alpha': '3.98e-01', 'height': 8.0}   93.10±4.46           15326.26±1714.58        17554.09±9150.35                        3.73\n",
      "{'alpha': '3.98e-01', 'height': 9.0}   94.36±3.77           17305.01±1961.98        16415.36±12038.10                       4.21\n",
      "{'alpha': '3.98e-01', 'height': 10.0}  94.07±3.53           18667.78±2363.40        17389.40±10546.27                       4.64\n",
      "{'alpha': '5.01e-01', 'height': 0.0}   16.87±0.30           16.99±0.33              16.88±2.47                              0.34\n",
      "{'alpha': '5.01e-01', 'height': 1.0}   7.27±0.20            8.34±0.48               8.26±2.08                               0.77\n",
      "{'alpha': '5.01e-01', 'height': 2.0}   36.08±68.23          134.34±173.38           133.99±151.31                           1.21\n",
      "{'alpha': '5.01e-01', 'height': 3.0}   949.82±1916.17       8193.65±10771.56        10610.38±20976.73                       1.91\n",
      "{'alpha': '5.01e-01', 'height': 4.0}   145.17±77.80         7900.59±3349.30         9002.25±12733.97                        2.16\n",
      "{'alpha': '5.01e-01', 'height': 5.0}   93.69±3.49           8217.00±1052.79         7326.23±4569.35                         2.84\n",
      "{'alpha': '5.01e-01', 'height': 6.0}   92.96±3.11           9679.85±1215.54         9288.33±6188.93                         3.2\n",
      "{'alpha': '5.01e-01', 'height': 7.0}   94.15±3.28           11136.78±1400.45        9455.93±5666.68                         3.85\n",
      "{'alpha': '5.01e-01', 'height': 8.0}   93.01±4.46           12167.92±1360.41        13935.10±7264.30                        3.73\n",
      "{'alpha': '5.01e-01', 'height': 9.0}   94.27±3.77           13740.09±1557.27        13036.04±9551.57                        4.21\n",
      "{'alpha': '5.01e-01', 'height': 10.0}  93.99±3.53           14822.46±1876.26        13805.70±8372.59                        4.64\n",
      "{'alpha': '6.31e-01', 'height': 0.0}   16.88±0.30           17.00±0.33              16.88±2.47                              0.34\n",
      "{'alpha': '6.31e-01', 'height': 1.0}   7.66±0.20            8.68±0.45               8.54±2.07                               0.77\n",
      "{'alpha': '6.31e-01', 'height': 2.0}   25.91±21.14          126.30±137.50           110.47±107.86                           1.21\n",
      "{'alpha': '6.31e-01', 'height': 3.0}   1732.59±7101.34      6325.81±7624.71         41977.44±202938.08                      1.91\n",
      "{'alpha': '6.31e-01', 'height': 4.0}   128.86±58.00         5931.38±2107.79         6551.50±8186.22                         2.16\n",
      "{'alpha': '6.31e-01', 'height': 5.0}   93.50±3.48           6521.26±833.69          5812.48±3625.02                         2.84\n",
      "{'alpha': '6.31e-01', 'height': 6.0}   92.80±3.10           7682.72±964.05          7372.77±4908.44                         3.2\n",
      "{'alpha': '6.31e-01', 'height': 7.0}   94.01±3.27           8840.25±1111.38         7503.93±4497.22                         3.85\n",
      "{'alpha': '6.31e-01', 'height': 8.0}   92.88±4.45           9659.25±1079.09         11060.50±5766.13                        3.73\n",
      "{'alpha': '6.31e-01', 'height': 9.0}   94.16±3.76           10908.44±1235.79        10351.80±7576.46                        4.21\n",
      "{'alpha': '6.31e-01', 'height': 10.0}  93.89±3.52           11768.08±1489.31        10959.13±6645.97                        4.64\n",
      "{'alpha': '7.94e-01', 'height': 0.0}   16.88±0.30           17.00±0.33              16.89±2.47                              0.34\n",
      "{'alpha': '7.94e-01', 'height': 1.0}   8.08±0.20            9.05±0.42               8.85±2.06                               0.77\n",
      "{'alpha': '7.94e-01', 'height': 2.0}   22.52±17.63          87.38±77.96             95.85±131.50                            1.21\n",
      "{'alpha': '7.94e-01', 'height': 3.0}   416.21±751.07        3714.85±3937.59         5486.40±14095.58                        1.91\n",
      "{'alpha': '7.94e-01', 'height': 4.0}   116.43±42.54         4500.38±1337.54         4842.28±5344.62                         2.16\n",
      "{'alpha': '7.94e-01', 'height': 5.0}   93.27±3.47           5174.43±659.66          4610.24±2874.86                         2.84\n",
      "{'alpha': '7.94e-01', 'height': 6.0}   92.60±3.09           6096.47±764.28          5851.31±3891.29                         3.2\n",
      "{'alpha': '7.94e-01', 'height': 7.0}   93.84±3.26           7016.15±881.76          5953.53±3568.26                         3.85\n",
      "{'alpha': '7.94e-01', 'height': 8.0}   92.73±4.43           7666.63±855.63          8777.22±4576.07                         3.73\n",
      "{'alpha': '7.94e-01', 'height': 9.0}   94.02±3.75           8659.26±980.43          8219.71±6007.58                         4.21\n",
      "{'alpha': '7.94e-01', 'height': 10.0}  93.76±3.51           9341.97±1181.93         8698.11±5274.44                         4.64\n",
      "{'alpha': '1.00e+00', 'height': 0.0}   16.89±0.30           17.01±0.33              16.90±2.47                              0.34\n",
      "{'alpha': '1.00e+00', 'height': 1.0}   8.51±0.21            9.45±0.39               9.19±2.06                               0.77\n",
      "{'alpha': '1.00e+00', 'height': 2.0}   14.74±7.03           50.36±21.22             55.99±60.86                             1.21\n",
      "{'alpha': '1.00e+00', 'height': 3.0}   295.46±297.82        2694.14±1517.29         2865.00±3303.75                         1.91\n",
      "{'alpha': '1.00e+00', 'height': 4.0}   106.99±30.60         3445.19±861.32          3628.78±3565.87                         2.16\n",
      "{'alpha': '1.00e+00', 'height': 5.0}   92.99±3.45           4104.79±521.44          3655.49±2278.93                         2.84\n",
      "{'alpha': '1.00e+00', 'height': 6.0}   92.36±3.07           4836.62±605.59          4642.95±3083.31                         3.2\n",
      "{'alpha': '1.00e+00', 'height': 7.0}   93.62±3.25           5567.36±699.37          4722.15±2830.33                         3.85\n",
      "{'alpha': '1.00e+00', 'height': 8.0}   92.54±4.42           6083.96±678.13          6963.66±3630.73                         3.73\n",
      "{'alpha': '1.00e+00', 'height': 9.0}   93.84±3.74           6872.78±777.59          6526.22±4761.39                         4.21\n",
      "{'alpha': '1.00e+00', 'height': 10.0}  93.59±3.50           7414.94±937.77          6902.24±4184.97                         4.64\n",
      "{'alpha': '1.26e+00', 'height': 0.0}   16.90±0.30           17.01±0.33              16.90±2.47                              0.34\n",
      "{'alpha': '1.26e+00', 'height': 1.0}   8.93±0.21            9.84±0.38               9.54±2.07                               0.77\n",
      "{'alpha': '1.26e+00', 'height': 2.0}   43.65±154.61         109.04±362.54           258.19±1136.96                          1.21\n",
      "{'alpha': '1.26e+00', 'height': 3.0}   218.99±247.31        2000.75±2238.28         2044.66±2944.52                         1.91\n",
      "{'alpha': '1.26e+00', 'height': 4.0}   99.85±21.55          2657.28±567.01          2752.63±2444.77                         2.16\n",
      "{'alpha': '1.26e+00', 'height': 5.0}   92.63±3.43           3255.36±411.67          2897.39±1805.48                         2.84\n",
      "{'alpha': '1.26e+00', 'height': 6.0}   92.05±3.06           3836.08±479.53          3683.33±2441.48                         3.2\n",
      "{'alpha': '1.26e+00', 'height': 7.0}   93.35±3.24           4416.71±554.49          3744.23±2244.14                         3.85\n",
      "{'alpha': '1.26e+00', 'height': 8.0}   92.31±4.40           4826.95±537.14          5523.24±2879.77                         3.73\n",
      "{'alpha': '1.26e+00', 'height': 9.0}   93.62±3.72           5453.87±616.47          5181.14±3771.51                         4.21\n",
      "{'alpha': '1.26e+00', 'height': 10.0}  93.39±3.49           5884.37±743.82          5475.86±3319.53                         4.64\n",
      "{'alpha': '1.58e+00', 'height': 0.0}   16.91±0.30           17.03±0.33              16.91±2.47                              0.34\n",
      "{'alpha': '1.58e+00', 'height': 1.0}   9.35±0.22            10.23±0.37              9.88±2.08                               0.77\n",
      "{'alpha': '1.58e+00', 'height': 2.0}   14.03±19.17          68.19±206.18            72.11±211.57                            1.21\n",
      "{'alpha': '1.58e+00', 'height': 3.0}   154.16±121.08        1421.38±1077.06         1718.64±2580.40                         1.91\n",
      "{'alpha': '1.58e+00', 'height': 4.0}   94.47±14.87          2062.16±384.28          2109.87±1727.50                         2.16\n",
      "{'alpha': '1.58e+00', 'height': 5.0}   92.20±3.40           2580.93±324.50          2295.57±1429.31                         2.84\n",
      "{'alpha': '1.58e+00', 'height': 6.0}   91.67±3.04           3041.58±379.38          2921.34±1931.61                         3.2\n",
      "{'alpha': '1.58e+00', 'height': 7.0}   93.02±3.22           3502.94±439.40          2967.68±1778.47                         3.85\n",
      "{'alpha': '1.58e+00', 'height': 8.0}   92.01±4.37           3828.67±425.16          4379.26±2283.20                         3.73\n",
      "{'alpha': '1.58e+00', 'height': 9.0}   93.34±3.71           4326.96±488.48          4112.86±2985.24                         4.21\n",
      "{'alpha': '1.58e+00', 'height': 10.0}  93.14±3.48           4668.75±589.75          4343.04±2632.05                         4.64\n",
      "{'alpha': '2.00e+00', 'height': 0.0}   16.92±0.30           17.04±0.33              16.93±2.47                              0.34\n",
      "{'alpha': '2.00e+00', 'height': 1.0}   9.74±0.23            10.60±0.36              10.21±2.10                              0.77\n",
      "{'alpha': '2.00e+00', 'height': 2.0}   10.81±4.32           28.05±7.90              31.82±31.22                             1.21\n",
      "{'alpha': '2.00e+00', 'height': 3.0}   153.04±299.54        1183.30±1684.97         1967.82±6958.57                         1.91\n",
      "{'alpha': '2.00e+00', 'height': 4.0}   90.43±10.14          1608.19±269.25          1631.03±1257.11                         2.16\n",
      "{'alpha': '2.00e+00', 'height': 5.0}   91.66±3.37           2045.57±255.29          1817.98±1130.37                         2.84\n",
      "{'alpha': '2.00e+00', 'height': 6.0}   91.21±3.01           2410.80±299.81          2316.42±1526.54                         3.2\n",
      "{'alpha': '2.00e+00', 'height': 7.0}   92.60±3.20           2777.39±347.98          2351.16±1408.51                         3.85\n",
      "{'alpha': '2.00e+00', 'height': 8.0}   91.65±4.34           3035.95±336.22          3470.79±1809.25                         3.73\n",
      "{'alpha': '2.00e+00', 'height': 9.0}   93.00±3.68           3432.03±386.81          3264.48±2360.70                         4.21\n",
      "{'alpha': '2.00e+00', 'height': 10.0}  92.83±3.46           3703.34±467.36          3443.43±2085.91                         4.64\n",
      "{'alpha': '2.51e+00', 'height': 0.0}   16.95±0.30           17.06±0.33              16.95±2.46                              0.34\n",
      "{'alpha': '2.51e+00', 'height': 1.0}   10.11±0.24           10.94±0.36              10.51±2.11                              0.77\n",
      "{'alpha': '2.51e+00', 'height': 2.0}   9.77±1.95            23.68±3.78              25.64±20.79                             1.21\n",
      "{'alpha': '2.51e+00', 'height': 3.0}   99.68±131.75         675.19±560.35           946.38±2494.04                          1.91\n",
      "{'alpha': '2.51e+00', 'height': 4.0}   87.37±7.01           1259.25±194.87          1269.72±937.80                          2.16\n",
      "{'alpha': '2.51e+00', 'height': 5.0}   91.01±3.33           1620.79±200.35          1439.20±892.75                          2.84\n",
      "{'alpha': '2.51e+00', 'height': 6.0}   90.65±2.98           1910.15±236.58          1836.34±1204.71                         3.2\n",
      "{'alpha': '2.51e+00', 'height': 7.0}   92.10±3.17           2201.41±275.34          1861.84±1114.57                         3.85\n",
      "{'alpha': '2.51e+00', 'height': 8.0}   91.21±4.31           2406.59±265.58          2749.47±1432.68                         3.73\n",
      "{'alpha': '2.51e+00', 'height': 9.0}   92.58±3.66           2721.45±306.05          2590.82±1864.62                         4.21\n",
      "{'alpha': '2.51e+00', 'height': 10.0}  92.45±3.43           2936.74±370.12          2729.14±1652.02                         4.64\n",
      "{'alpha': '3.16e+00', 'height': 0.0}   16.98±0.29           17.09±0.33              16.99±2.46                              0.34\n",
      "{'alpha': '3.16e+00', 'height': 1.0}   10.46±0.24           11.26±0.36              10.80±2.13                              0.77\n",
      "{'alpha': '3.16e+00', 'height': 2.0}   9.46±1.13            21.12±2.31              22.90±15.77                             1.21\n",
      "{'alpha': '3.16e+00', 'height': 3.0}   67.90±35.26          462.28±318.98           470.69±599.28                           1.91\n",
      "{'alpha': '3.16e+00', 'height': 4.0}   85.01±5.13           989.52±144.87           994.20±712.51                           2.16\n",
      "{'alpha': '3.16e+00', 'height': 5.0}   90.23±3.27           1283.96±156.75          1139.06±703.78                          2.84\n",
      "{'alpha': '3.16e+00', 'height': 6.0}   89.96±2.94           1512.99±186.33          1455.56±948.97                          3.2\n",
      "{'alpha': '3.16e+00', 'height': 7.0}   91.48±3.14           1744.35±217.64          1473.67±880.98                          3.85\n",
      "{'alpha': '3.16e+00', 'height': 8.0}   90.66±4.26           1907.06±209.48          2176.88±1133.44                         3.73\n",
      "{'alpha': '3.16e+00', 'height': 9.0}   92.06±3.62           2157.36±241.90          2056.01±1470.60                         4.21\n",
      "{'alpha': '3.16e+00', 'height': 10.0}  91.98±3.40           2328.12±292.87          2162.13±1307.27                         4.64\n",
      "{'alpha': '3.98e+00', 'height': 0.0}   17.02±0.29           17.13±0.33              17.03±2.45                              0.34\n",
      "{'alpha': '3.98e+00', 'height': 1.0}   10.78±0.25           11.55±0.37              11.06±2.14                              0.77\n",
      "{'alpha': '3.98e+00', 'height': 2.0}   9.50±0.81            19.59±1.79              20.98±11.69                             1.21\n",
      "{'alpha': '3.98e+00', 'height': 3.0}   58.16±24.67          342.02±218.09           341.45±370.57                           1.91\n",
      "{'alpha': '3.98e+00', 'height': 4.0}   83.11±4.15           780.13±109.73           782.43±547.58                           2.16\n",
      "{'alpha': '3.98e+00', 'height': 5.0}   89.30±3.21           1017.16±122.18          901.60±553.41                           2.84\n",
      "{'alpha': '3.98e+00', 'height': 6.0}   89.14±2.90           1198.17±146.36          1153.78±745.72                          3.2\n",
      "{'alpha': '3.98e+00', 'height': 7.0}   90.74±3.10           1381.86±171.78          1165.99±695.31                          3.85\n",
      "{'alpha': '3.98e+00', 'height': 8.0}   90.00±4.20           1510.79±164.92          1722.53±895.58                          3.73\n",
      "{'alpha': '3.98e+00', 'height': 9.0}   91.43±3.58           1709.73±190.92          1631.58±1157.64                         4.21\n",
      "{'alpha': '3.98e+00', 'height': 10.0}  91.40±3.37           1845.08±231.47          1712.21±1033.31                         4.64\n",
      "{'alpha': '5.01e+00', 'height': 0.0}   17.08±0.29           17.19±0.33              17.09±2.45                              0.34\n",
      "{'alpha': '5.01e+00', 'height': 1.0}   11.07±0.26           11.83±0.37              11.30±2.16                              0.77\n",
      "{'alpha': '5.01e+00', 'height': 2.0}   9.73±0.65            18.68±1.39              19.84±8.73                              1.21\n",
      "{'alpha': '5.01e+00', 'height': 3.0}   51.88±17.50          258.25±142.92           260.60±244.84                           1.91\n",
      "{'alpha': '5.01e+00', 'height': 4.0}   81.47±3.68           617.24±83.95            618.83±423.12                           2.16\n",
      "{'alpha': '5.01e+00', 'height': 5.0}   88.22±3.13           806.19±94.78            714.16±433.64                           2.84\n",
      "{'alpha': '5.01e+00', 'height': 6.0}   88.18±2.84           948.93±114.56           914.94±584.13                           3.2\n",
      "{'alpha': '5.01e+00', 'height': 7.0}   89.86±3.05           1094.66±135.32          922.41±547.66                           3.85\n",
      "{'alpha': '5.01e+00', 'height': 8.0}   89.21±4.13           1196.66±129.55          1362.23±706.43                          3.73\n",
      "{'alpha': '5.01e+00', 'height': 9.0}   90.68±3.53           1354.74±150.41          1294.92±909.07                          4.21\n",
      "{'alpha': '5.01e+00', 'height': 10.0}  90.71±3.32           1461.90±182.67          1355.43±815.54                          4.64\n",
      "{'alpha': '6.31e+00', 'height': 0.0}   17.16±0.29           17.27±0.33              17.18±2.44                              0.34\n",
      "{'alpha': '6.31e+00', 'height': 1.0}   11.36±0.26           12.08±0.37              11.53±2.17                              0.77\n",
      "{'alpha': '6.31e+00', 'height': 2.0}   10.10±0.53           18.17±1.07              19.18±6.70                              1.21\n",
      "{'alpha': '6.31e+00', 'height': 3.0}   47.45±12.16          198.75±90.65            204.70±165.16                           1.91\n",
      "{'alpha': '6.31e+00', 'height': 4.0}   79.97±3.44           490.55±64.40            492.14±327.07                           2.16\n",
      "{'alpha': '6.31e+00', 'height': 5.0}   86.97±3.04           639.80±73.10            566.76±338.12                           2.84\n",
      "{'alpha': '6.31e+00', 'height': 6.0}   87.05±2.77           752.00±89.23            726.30±455.63                           3.2\n",
      "{'alpha': '6.31e+00', 'height': 7.0}   88.82±2.99           867.44±106.33           730.00±430.17                           3.85\n",
      "{'alpha': '6.31e+00', 'height': 8.0}   88.28±4.04           947.95±101.46           1076.81±555.93                          3.73\n",
      "{'alpha': '6.31e+00', 'height': 9.0}   89.77±3.46           1073.47±118.21          1028.13±711.65                          4.21\n",
      "{'alpha': '6.31e+00', 'height': 10.0}  89.87±3.27           1158.18±143.86          1072.79±642.36                          4.64\n",
      "{'alpha': '7.94e+00', 'height': 0.0}   17.28±0.29           17.38±0.33              17.29±2.42                              0.34\n",
      "{'alpha': '7.94e+00', 'height': 1.0}   11.63±0.26           12.34±0.38              11.75±2.18                              0.77\n",
      "{'alpha': '7.94e+00', 'height': 2.0}   10.58±0.45           17.98±0.83              18.89±5.44                              1.21\n",
      "{'alpha': '7.94e+00', 'height': 3.0}   44.29±8.20           156.80±56.11            165.02±112.86                           1.91\n",
      "{'alpha': '7.94e+00', 'height': 4.0}   78.55±3.28           392.30±49.23            394.11±251.78                           2.16\n",
      "{'alpha': '7.94e+00', 'height': 5.0}   85.59±2.93           509.11±55.99            451.50±261.80                           2.84\n",
      "{'alpha': '7.94e+00', 'height': 6.0}   85.78±2.69           596.90±69.01            577.79±353.40                           3.2\n",
      "{'alpha': '7.94e+00', 'height': 7.0}   87.62±2.91           688.11±83.24            578.51±336.57                           3.85\n",
      "{'alpha': '7.94e+00', 'height': 8.0}   87.19±3.93           751.44±79.15            851.07±436.06                           3.73\n",
      "{'alpha': '7.94e+00', 'height': 9.0}   88.72±3.38           850.96±92.59            816.99±554.85                           4.21\n",
      "{'alpha': '7.94e+00', 'height': 10.0}  88.89±3.20           917.75±112.97           849.25±504.54                           4.64\n",
      "{'alpha': '1.00e+01', 'height': 0.0}   17.43±0.29           17.53±0.33              17.45±2.41                              0.34\n",
      "{'alpha': '1.00e+01', 'height': 1.0}   11.90±0.26           12.59±0.38              11.98±2.19                              0.77\n",
      "{'alpha': '1.00e+01', 'height': 2.0}   11.17±0.39           18.02±0.67              18.87±4.76                              1.21\n",
      "{'alpha': '1.00e+01', 'height': 3.0}   42.13±5.36           127.52±34.14            136.74±77.89                            1.91\n",
      "{'alpha': '1.00e+01', 'height': 4.0}   77.19±3.11           316.62±37.29            318.65±192.18                           2.16\n",
      "{'alpha': '1.00e+01', 'height': 5.0}   84.10±2.79           407.11±42.51            362.15±200.74                           2.84\n",
      "{'alpha': '1.00e+01', 'height': 6.0}   84.37±2.59           475.34±52.86            461.46±272.07                           3.2\n",
      "{'alpha': '1.00e+01', 'height': 7.0}   86.27±2.82           547.09±64.83            459.88±261.91                           3.85\n",
      "{'alpha': '1.00e+01', 'height': 8.0}   85.96±3.80           596.64±61.45            672.99±340.46                           3.73\n",
      "{'alpha': '1.00e+01', 'height': 9.0}   87.50±3.28           675.34±72.19            650.27±430.32                           4.21\n",
      "{'alpha': '1.00e+01', 'height': 10.0}  87.74±3.11           727.82±88.37            672.92±394.76                           4.64\n",
      "{'alpha': '1.26e+01', 'height': 0.0}   17.63±0.28           17.73±0.33              17.66±2.39                              0.34\n",
      "{'alpha': '1.26e+01', 'height': 1.0}   12.19±0.26           12.87±0.37              12.21±2.21                              0.77\n",
      "{'alpha': '1.26e+01', 'height': 2.0}   11.84±0.35           18.26±0.56              19.07±4.47                              1.21\n",
      "{'alpha': '1.26e+01', 'height': 3.0}   40.83±3.49           107.38±20.59            116.71±54.13                            1.91\n",
      "{'alpha': '1.26e+01', 'height': 4.0}   75.97±2.93           258.94±27.83            261.13±144.73                           2.16\n",
      "{'alpha': '1.26e+01', 'height': 5.0}   82.58±2.63           328.24±31.96            293.75±151.86                           2.84\n",
      "{'alpha': '1.26e+01', 'height': 6.0}   82.89±2.47           380.76±39.94            371.00±207.38                           3.2\n",
      "{'alpha': '1.26e+01', 'height': 7.0}   84.81±2.71           436.81±50.12            367.74±202.23                           3.85\n",
      "{'alpha': '1.26e+01', 'height': 8.0}   84.59±3.65           475.27±47.40            533.07±264.09                           3.73\n",
      "{'alpha': '1.26e+01', 'height': 9.0}   86.13±3.15           537.26±55.94            519.09±331.42                           4.21\n",
      "{'alpha': '1.26e+01', 'height': 10.0}  86.44±3.01           578.24±68.76            534.38±307.20                           4.64\n",
      "{'alpha': '1.58e+01', 'height': 0.0}   17.90±0.28           17.99±0.33              17.93±2.39                              0.34\n",
      "{'alpha': '1.58e+01', 'height': 1.0}   12.50±0.26           13.17±0.37              12.48±2.23                              0.77\n",
      "{'alpha': '1.58e+01', 'height': 2.0}   12.61±0.32           18.64±0.50              19.44±4.40                              1.21\n",
      "{'alpha': '1.58e+01', 'height': 3.0}   40.35±2.40           93.85±12.46             102.75±37.78                            1.91\n",
      "{'alpha': '1.58e+01', 'height': 4.0}   74.98±2.72           215.71±20.36            217.94±106.92                           2.16\n",
      "{'alpha': '1.58e+01', 'height': 5.0}   81.15±2.44           268.09±23.74            242.30±112.79                           2.84\n",
      "{'alpha': '1.58e+01', 'height': 6.0}   81.43±2.34           308.00±29.63            301.44±156.01                           3.2\n",
      "{'alpha': '1.58e+01', 'height': 7.0}   83.29±2.58           351.31±38.36            297.07±154.48                           3.85\n",
      "{'alpha': '1.58e+01', 'height': 8.0}   83.15±3.46           380.82±36.26            423.79±202.98                           3.73\n",
      "{'alpha': '1.58e+01', 'height': 9.0}   84.65±3.01           429.31±42.96            416.43±252.89                           4.21\n",
      "{'alpha': '1.58e+01', 'height': 10.0}  85.01±2.89           461.04±53.12            426.22±237.23                           4.64\n",
      "{'alpha': '2.00e+01', 'height': 0.0}   18.23±0.28           18.32±0.33              18.27±2.40                              0.34\n",
      "{'alpha': '2.00e+01', 'height': 1.0}   12.86±0.26           13.53±0.36              12.79±2.27                              0.77\n",
      "{'alpha': '2.00e+01', 'height': 2.0}   13.45±0.30           19.15±0.46              19.94±4.45                              1.21\n",
      "{'alpha': '2.00e+01', 'height': 3.0}   40.65±1.85           85.13±7.73              93.33±26.52                             1.91\n",
      "{'alpha': '2.00e+01', 'height': 4.0}   74.40±2.48           184.06±14.51            186.21±76.96                            2.16\n",
      "{'alpha': '2.00e+01', 'height': 5.0}   79.96±2.23           223.10±17.38            204.52±81.76                            2.84\n",
      "{'alpha': '2.00e+01', 'height': 6.0}   80.10±2.19           252.89±21.47            248.81±115.36                           3.2\n",
      "{'alpha': '2.00e+01', 'height': 7.0}   81.83±2.43           285.81±28.96            243.84±116.27                           3.85\n",
      "{'alpha': '2.00e+01', 'height': 8.0}   81.72±3.25           308.10±27.44            339.25±154.00                           3.73\n",
      "{'alpha': '2.00e+01', 'height': 9.0}   83.12±2.83           345.63±32.60            336.75±190.60                           4.21\n",
      "{'alpha': '2.00e+01', 'height': 10.0}  83.50±2.74           369.90±40.62            342.55±181.25                           4.64\n",
      "{'alpha': '2.51e+01', 'height': 0.0}   18.65±0.28           18.73±0.32              18.70±2.46                              0.34\n",
      "{'alpha': '2.51e+01', 'height': 1.0}   13.28±0.26           13.95±0.36              13.16±2.32                              0.77\n",
      "{'alpha': '2.51e+01', 'height': 2.0}   14.39±0.28           19.77±0.44              20.57±4.58                              1.21\n",
      "{'alpha': '2.51e+01', 'height': 3.0}   41.75±1.57           79.91±5.03              87.37±18.91                             1.91\n",
      "{'alpha': '2.51e+01', 'height': 4.0}   74.41±2.23           161.65±10.04            163.59±53.54                            2.16\n",
      "{'alpha': '2.51e+01', 'height': 5.0}   79.23±2.01           190.36±12.49            177.62±57.48                            2.84\n",
      "{'alpha': '2.51e+01', 'height': 6.0}   79.10±2.03           212.05±15.10            209.89±83.43                            3.2\n",
      "{'alpha': '2.51e+01', 'height': 7.0}   80.56±2.25           236.51±21.47            204.74±85.84                            3.85\n",
      "{'alpha': '2.51e+01', 'height': 8.0}   80.41±3.00           252.97±20.47            274.72±114.79                           3.73\n",
      "{'alpha': '2.51e+01', 'height': 9.0}   81.65±2.62           281.59±24.35            275.63±141.30                           4.21\n",
      "{'alpha': '2.51e+01', 'height': 10.0}  82.00±2.57           299.81±30.65            278.69±136.47                           4.64\n",
      "{'alpha': '3.16e+01', 'height': 0.0}   19.17±0.27           19.23±0.32              19.21±2.56                              0.34\n",
      "{'alpha': '3.16e+01', 'height': 1.0}   13.79±0.25           14.46±0.35              13.61±2.41                              0.77\n",
      "{'alpha': '3.16e+01', 'height': 2.0}   15.43±0.27           20.50±0.42              21.31±4.76                              1.21\n",
      "{'alpha': '3.16e+01', 'height': 3.0}   43.65±1.38           77.31±3.45              84.07±14.15                             1.91\n",
      "{'alpha': '3.16e+01', 'height': 4.0}   76.17±3.54           146.54±6.82             151.52±45.55                            2.16\n",
      "{'alpha': '3.16e+01', 'height': 5.0}   79.16±1.77           167.37±8.78             159.23±38.99                            2.84\n",
      "{'alpha': '3.16e+01', 'height': 6.0}   78.63±1.86           182.68±10.26            181.97±58.69                            3.2\n",
      "{'alpha': '3.16e+01', 'height': 7.0}   79.69±2.05           200.26±15.54            176.96±61.89                            3.85\n",
      "{'alpha': '3.16e+01', 'height': 8.0}   79.41±2.72           212.10±14.99            226.45±83.52                            3.73\n",
      "{'alpha': '3.16e+01', 'height': 9.0}   80.39±2.38           233.45±17.82            229.54±102.51                           4.21\n",
      "{'alpha': '3.16e+01', 'height': 10.0}  80.65±2.38           246.77±22.72            230.83±100.77                           4.64\n",
      "{'alpha': '3.98e+01', 'height': 0.0}   19.79±0.27           19.83±0.31              19.82±2.74                              0.34\n",
      "{'alpha': '3.98e+01', 'height': 1.0}   14.40±0.24           15.07±0.34              14.16±2.53                              0.77\n",
      "{'alpha': '3.98e+01', 'height': 2.0}   16.58±0.26           21.35±0.41              22.17±4.97                              1.21\n",
      "{'alpha': '3.98e+01', 'height': 3.0}   46.79±2.68           76.88±2.73              82.96±11.89                             1.91\n",
      "{'alpha': '3.98e+01', 'height': 4.0}   78.29±2.73           136.89±4.35             138.18±23.28                            2.16\n",
      "{'alpha': '3.98e+01', 'height': 5.0}   81.36±4.67           151.98±6.04             147.27±25.64                            2.84\n",
      "{'alpha': '3.98e+01', 'height': 6.0}   78.89±1.68           162.33±6.73             162.72±39.97                            3.2\n",
      "{'alpha': '3.98e+01', 'height': 7.0}   79.42±1.83           174.45±10.90            158.04±43.47                            3.85\n",
      "{'alpha': '3.98e+01', 'height': 8.0}   78.91±2.42           182.70±10.71            191.34±58.90                            3.73\n",
      "{'alpha': '3.98e+01', 'height': 9.0}   79.54±2.12           198.14±12.72            195.60±72.32                            4.21\n",
      "{'alpha': '3.98e+01', 'height': 10.0}  79.61±2.17           207.53±16.46            195.86±72.56                            4.64\n",
      "{'alpha': '5.01e+01', 'height': 0.0}   20.52±0.26           20.54±0.31              20.54±2.98                              0.34\n",
      "{'alpha': '5.01e+01', 'height': 1.0}   15.14±0.24           15.79±0.32              14.83±2.68                              0.77\n",
      "{'alpha': '5.01e+01', 'height': 2.0}   17.86±0.26           22.33±0.41              23.15±5.20                              1.21\n",
      "{'alpha': '5.01e+01', 'height': 3.0}   52.60±8.93           78.05±2.31              83.97±11.44                             1.91\n",
      "{'alpha': '5.01e+01', 'height': 4.0}   90.36±40.64          131.38±2.68             132.09±14.10                            2.16\n",
      "{'alpha': '5.01e+01', 'height': 5.0}   86.57±11.43          142.31±4.13             140.01±17.08                            2.84\n",
      "{'alpha': '5.01e+01', 'height': 6.0}   80.30±1.86           148.92±4.28             150.11±26.43                            3.2\n",
      "{'alpha': '5.01e+01', 'height': 7.0}   80.66±4.28           156.80±7.34             145.84±29.88                            3.85\n",
      "{'alpha': '5.01e+01', 'height': 8.0}   102.05±125.25        162.36±7.42             166.75±39.97                            3.73\n",
      "{'alpha': '5.01e+01', 'height': 9.0}   79.31±1.84           173.06±8.82             171.40±49.30                            4.21\n",
      "{'alpha': '5.01e+01', 'height': 10.0}  79.11±1.94           179.38±11.59            171.13±50.68                            4.64\n",
      "{'alpha': '6.31e+01', 'height': 0.0}   21.37±0.25           21.36±0.30              21.36±3.30                              0.34\n",
      "{'alpha': '6.31e+01', 'height': 1.0}   16.00±0.23           16.64±0.31              15.61±2.88                              0.77\n",
      "{'alpha': '6.31e+01', 'height': 2.0}   19.28±0.27           23.44±0.40              24.28±5.45                              1.21\n",
      "{'alpha': '6.31e+01', 'height': 3.0}   67.87±29.82          91.16±26.51             96.98±39.72                             1.91\n",
      "{'alpha': '6.31e+01', 'height': 4.0}   107.49±25.27         128.93±2.74             128.75±10.99                            2.16\n",
      "{'alpha': '6.31e+01', 'height': 5.0}   89.83±10.32          136.76±2.90             136.01±12.97                            2.84\n",
      "{'alpha': '6.31e+01', 'height': 6.0}   161.06±145.43        140.65±2.71             142.38±17.50                            3.2\n",
      "{'alpha': '6.31e+01', 'height': 7.0}   81.91±2.06           145.37±4.70             138.51±20.55                            3.85\n",
      "{'alpha': '6.31e+01', 'height': 8.0}   88.96±46.20          148.99±4.98             150.41±26.08                            3.73\n",
      "{'alpha': '6.31e+01', 'height': 9.0}   81.92±6.57           155.98±5.92             154.85±32.39                            4.21\n",
      "{'alpha': '6.31e+01', 'height': 10.0}  79.73±2.65           159.98±7.89             154.36±34.30                            4.64\n",
      "{'alpha': '7.94e+01', 'height': 0.0}   22.33±0.25           22.28±0.29              22.30±3.67                              0.34\n",
      "{'alpha': '7.94e+01', 'height': 1.0}   17.00±0.22           17.61±0.31              16.51±3.11                              0.77\n",
      "{'alpha': '7.94e+01', 'height': 2.0}   20.87±0.29           24.71±0.40              25.57±5.70                              1.21\n",
      "{'alpha': '7.94e+01', 'height': 3.0}   96.00±137.36         88.45±21.19             116.48±122.17                           1.91\n",
      "{'alpha': '7.94e+01', 'height': 4.0}   90.17±2.70           127.83±1.25             127.78±10.19                            2.16\n",
      "{'alpha': '7.94e+01', 'height': 5.0}   93.16±5.52           134.03±2.21             134.18±12.01                            2.84\n",
      "{'alpha': '7.94e+01', 'height': 6.0}   90.97±12.29          136.03±1.86             138.07±12.59                            3.2\n",
      "{'alpha': '7.94e+01', 'height': 7.0}   86.97±5.50           138.51±2.86             134.56±14.87                            3.85\n",
      "{'alpha': '7.94e+01', 'height': 8.0}   83.75±2.20           140.78±3.27             140.27±16.82                            3.73\n",
      "{'alpha': '7.94e+01', 'height': 9.0}   84.03±8.84           144.96±3.86             144.16±20.91                            4.21\n",
      "{'alpha': '7.94e+01', 'height': 10.0}  86.48±17.93          147.31±5.22             143.58±22.86                            4.64\n",
      "{'alpha': '1.00e+02', 'height': 0.0}   23.39±0.25           23.31±0.29              23.34±4.08                              0.34\n",
      "{'alpha': '1.00e+02', 'height': 1.0}   18.14±0.22           18.69±0.30              17.54±3.38                              0.77\n",
      "{'alpha': '1.00e+02', 'height': 2.0}   22.64±0.31           26.16±0.40              27.03±5.94                              1.21\n",
      "{'alpha': '1.00e+02', 'height': 3.0}   111.74±210.77        121.27±177.90           113.99±119.23                           1.91\n",
      "{'alpha': '1.00e+02', 'height': 4.0}   93.79±1.45           128.34±1.05             128.11±10.39                            2.16\n",
      "{'alpha': '1.00e+02', 'height': 5.0}   94.00±2.46           133.10±1.88             133.70±12.23                            2.84\n",
      "{'alpha': '1.00e+02', 'height': 6.0}   91.27±2.73           133.86±1.50             136.04±10.62                            3.2\n",
      "{'alpha': '1.00e+02', 'height': 7.0}   127.20±47.94         134.86±1.73             132.82±11.96                            3.85\n",
      "{'alpha': '1.00e+02', 'height': 8.0}   87.47±4.62           136.21±2.22             134.58±11.84                            3.73\n",
      "{'alpha': '1.00e+02', 'height': 9.0}   87.22±6.65           138.38±2.54             137.76±14.40                            4.21\n",
      "{'alpha': '1.00e+02', 'height': 10.0}  111.65±77.03         139.62±3.43             137.15±16.01                            4.64\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "calculate performance: 100%|██████████| 13530/13530 [13:10<00:00, 20.24it/s]"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = weisfeilerlehmankernel\n",
    "param_grid_precomputed = {'height': np.linspace(0, 10, 11)}\n",
    "param_grid = {'alpha': np.logspace(-2, 2, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 0.8382418155670166 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 0.0} is: \n",
      "[[ 2.  1.  2. ...  0.  0.  1.]\n",
      " [ 1.  3.  1. ...  0.  0.  2.]\n",
      " [ 2.  1.  2. ...  0.  0.  1.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 10.  7.  0.]\n",
      " [ 0.  0.  0. ...  7. 10.  1.]\n",
      " [ 1.  2.  1. ...  0.  1. 10.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddfaa7bb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 1.797783374786377 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 1.0} is: \n",
      "[[ 4.  2.  4. ...  0.  0.  2.]\n",
      " [ 2.  6.  2. ...  0.  0.  4.]\n",
      " [ 4.  2.  4. ...  0.  0.  2.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 20. 14.  0.]\n",
      " [ 0.  0.  0. ... 14. 20.  2.]\n",
      " [ 2.  4.  2. ...  0.  2. 20.]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde3ecd9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 2.571061372756958 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 2.0} is: \n",
      "[[ 6.  3.  6. ...  0.  0.  3.]\n",
      " [ 3.  9.  3. ...  0.  0.  6.]\n",
      " [ 6.  3.  6. ...  0.  0.  3.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 30. 21.  0.]\n",
      " [ 0.  0.  0. ... 21. 30.  3.]\n",
      " [ 3.  6.  3. ...  0.  3. 30.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf89a64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 3.4686691761016846 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 3.0} is: \n",
      "[[ 8.  4.  8. ...  0.  0.  4.]\n",
      " [ 4. 12.  4. ...  0.  0.  8.]\n",
      " [ 8.  4.  8. ...  0.  0.  4.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 40. 28.  0.]\n",
      " [ 0.  0.  0. ... 28. 40.  4.]\n",
      " [ 4.  8.  4. ...  0.  4. 40.]]\n"
     ]
    },
    {
     "data": {
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Ahgwg9k3Lktux1h7Orw1LYK1QSe4qYYngO7PWJ0t2zrStYZW6akmzlhLbryvSRV2T/pAitk1k12LC40hY7e4qxk/PhWdVXODHNX56BGf2nAt1kyaWzmJjbqyGPozrqpSieo9/ezJiW67bvu6arwBc64LKswveLqoIqCV90ChifGaXUy/7pxsF76sugGV1k7Jk+8rxU95X/UgxzmoercmPrL8VAQjGg3NQpPQHlvQdaF/Zlm5C1GH0TFneJ1kEpupn+H5aRpTy+mFJ76tOKzo4B1hDVD/Sku/H69dlTwWwXWJaQLuD6kKnjL/ZmXJNe5udqcAxFDiLehm9b3EWMwtb28JZkCuw173gNN4pqmNQ5Wh4+X1wFl4sDA8LoLE3lBOz4QpZbw+o1e2ZG8PvbhWjoofKxm/xED04OAvE79ealXVhhD7IYtVNJNVfnRv7bbXkmq1fsRJ8PsTkMDqvGgfRR5+jc5tSK0acjtGzcL5CgBxu5F7IPUf349FVr+D0cA66WDqPfLe2SEibnSl0HvluEFk6j3y3auOWgxX7f8tBtLqrESaD9SnoSxe+npZLpQhgxRgSFX8AXFgux7R5y0H0gFq/iCMJ5RJtTJf/azAfLuCejJe/lwyGpYLNx/UGBaxeR/wedNHZAEOt/Z1YAWesM5aj1GthnhPAI0vP3j6H+XvjMipuRJsbKlHCclVL97wt9NcuMWs1UssH7y8dGquU3RisnLWiGBWXei225sgmXH43usFyTOx7a38HZzeeTLZvqVFwNtRQQ0PRjuAsUrQ+P4p2t/5bT3tgqjSdxmaiqowqPqu6KIbQ9Dl99FTNdNru1nUOK4dn0Xmkkitj02mloCQoDIfmwmkSycb7OxVbbmRLEk9uazptd6V+ILRRw2+Q5ZbTuTidygaOHItOv/X50Vq9NJ2mUJT2ZCxO8Wq8vcVuzcuWNLOwFRTF8/c+HnnXqtjAeV8+RKVyyYL3QabadrTfrJf6Bb6z5UMFBxI4DKMbiPvbjeqrTKrdMA/6Pba7q5jB1LaQp8pRUVmt80sztfcOUqZTy2W1Nq5AUFbryLGKPSoHP21kdf5m3EygFAOOZ9iYQ83qQNZ3+nih7PXEl+njs1g5kGNiqVjowZZfWjm0LACsHMiDl2vxfBYwFpZNZJxQ6ydCb0yLPdjsTFVyczkPAXxUluO4+fGRFLzEj399fraGyZhZ2ApWl2l5ByqSaL1svw44q3ty9m45GC3MlcOzmCjFNPs8f9s56pVilpbjuBmWQK0hw8SkJFuuOgB+C/QQBRDVr0pAjtPOuRLHrRuLUqycj71KlTRgb+jv/k4QabV924Ze3+xMhf9JLadfV5zOoh9Zl2D74RWKzwJAxV1/YikGPpE7efb2OWzMjUUTNn30FCaWpkoOIY4RoCZN/iZXUXErp4ASSm4/JM/cSx2HurMzuLB1VFNXfM90qspUNfNq/hWaSVUP8eztcyHWgbVQtPZ3XCVtS7gQLghaUABj4jVoUXJI0Ydb1md1HCHdgi4aRz9SULzRsI5hSK0hihxWi4yiXlXpDtR1FrqwWS4Clkm7w/Y18g0p9U6RLsR5TxHy03CE1trUW+xuy0V9x28W1lxkFXkeFkKVVvZE23UyR+vIsSjS9Pr8KKaPF1wDxRPV0vNFt7sIog25FUB8DzpTUT8oQqyXp4me9EDFCVnLCutUZa4u4IAGLJ9hG7Z+oDqRWW/rSDeIS3oyrxyerfKqSL0BfdmZqrTvjva/XdYRxZcgB+MsdirgNGGOcjCqdF4ruaAUrmQYBKeXL4McgxUNeF2tJB7+QsmaX+1C9XAwnhjn5SWJ6vZwIaYOvrdpxL4gk6bsdhGcVyQoy2OHAUQmTmVP+923ACUFdnlmR4oakUNPucnYdrXvHI9eV/MlE8hEi9exsngmUp0fe53jJbEdbkCD6uUJ6QG0bKYxPmsBc/ZvW97rf7+ynoWM5G0cnp5AgW12MaoJ1xsH+6vkZSQbZryev43tq+UctS9ePhO2befRbnit/R08/sKDTa7Thhpq6OLSjhBDsrFR9N5+sLZ7WzY1/HZs78oBtI50I8Qnibvt5INPBDEEqMBam52pgJ/41icmMfZ0Fikz291V7OvG2IqVw7OBC5k+egrf+kTBQO69/5oifeLJHBOCvbBQc/adp78q9KxuBqg03aq0ZBv96rdcDOeUxPosPJz6Hsut8ITSebQ6F94nN+ZhZZTt1vFabAv1LNonezq39nfc09oiIQFESlTqunadzN1UllS+9245WEtTGFt84vSFijJmPwZlUNO+Lt3zNgCIvKrVQkjRSrmg1v4ibWG7W7cm8hkNT3juO1cggtOzhgCivDGsfNhEgKDVV2AMy3j31R2b5lG1IADA2NMZtm56CXg4DQwDKhMtF/fY02NlmZMhI5oHmNIXyUVfQNHjftiXrYpKzcqWagMo3fhRbUJceLpYWbcqhFP1qWJxTZ7fmMsw+aDog1DkgrWBikjaFt893wWtUhNLcd+U+sHTbTsrh2cjPYCWTfWv9j31mRtrlbG6o1D/ANOpbp5WEV/UER8oVMTqt8mynqXIXtuONWTH6CwuZlg969DV774nh2vZb31iEnvvvyZqz5PvPQcwfqRWf2Kh4YMUZz05ZWg/18WjbXiKNDt266vCvlgshZ0nz/RJUr2GF3JOEae2PzpvVjGY4jZSugWtW5/36rW6JFUm27nQOWEfvI2JY/CianlWGzsGRb16VinPWdErY50CSRZ2vx2dxY7gLLKxUlNvXlDEtsmHqKyvsun8WNShy344ZJXtS+cJ1TKb0t77r8Fzd70MADi33I4Un9oH4ia8E17bWjmQB/Mt4d40zaX8MNbee3MUCdwLAR8iKZX1AxX7OqhfQHGSeRB57zkAEVsMoIbZAGKsiIK9irD2cd32FPfuKXmL2TMZ8t161jTOuVXoaj5ZoO4BrG1ZJaPFaGhb3kFX809x+kFi+/bbpoiu11QssW0pbceRrFFwNtRQQ0PRjuAs8q16jEQANfivgmaicua3NWHqKaCmQ71HHYIFUAEFRwEAIzObAGKFJGVTL4Bvb7EbYRxSFNzpUe3+HmekbSvop18bhexfjdeOn7qBSQhQSepV8k74oFsQfIQ1VXMuLUdUjK1epwWL9Ss7KE4Gr/WTzKtn4vo5NkZMS/UBkFNbuNt0O8ORFVXaZR8VMwPEpvF2NwbDWQ7sQsLq7RidRQpnoXBv/rb6CCrE+DFabITFGQCxzKw4iUFwb3qcqq4iBfdO6Vu8YDiekpI4CL3XDw9h6weqzUzh3uy3nUeryPXqBqqPWIPfAHWsAQFdns1fcQPeHHlu4h5+pJbMxyGrS9G+1UQICcCjZW0AnxTOIoVBse8mpVOwc6j9DyLVAF2X58HMOi8EZ7EjOIt+plN1zebvuo9HlXWMz8fyd51beOZDewAAnUfOyrNVPSnF2us/e9rkJamemT/SDfqU5YSLOrkQGzuDruCRwmp/p5aRjH+nXNSVy6GZuXXkWBQPsrfYLUyV8kGrmdOr17NE1HKYcOGV1zRLl62LY6i5XLPPYuLc7EzVNqveLQdDBrVBZJ3b2DdF+bJfes62zDi0v+rmnYqh0a8fKeqXpyW0T2WoU45w75SuyY7x7N+4tm9/lHY8Z0FKIeqUvBSEqft212fgXpI1iQIVjqDdXQ2bjWIs+lkI2B/PSsKF7yUx8hRiXlg9PXnsJpfyDVFxyloY+s2/7Qfn0PbXWloi5aJjcfAsG57VQ+tPKQyVPM7CziHLKXai3/eW4mZsHXrdjssj6xOjbXnzZa9by4p3T/u1nbB6jYKzoYYaGop2BGeRykimcjV1D4onABD8OTQQr57e1t/DsmfkKGy9QB1Yo3Wyr898aE/kpq4ntQ0PP4gzAnz7u+I/+qUCGIQ3ABDpVewp1C8VQD+MgNLSPW+rxbO4kFQAioFQfZI3Zynql0qQ/QPKVADSDy3Ld1BL7yDj1usaVYtkRamUCKP9BipukHoxbcMTa9h2LZ5FidfR9/ON7zyEv/rLF64cnUW/eBbKtgWvSEGpAUVMCiCPNhkGyanu+5sQA9dAcApA2pFsY66AgAcbfPBWPVtTGqZiH7AuC5TSGBMrh2cj7EIYayKeRaoNjjf0qcxtwlgOim/w4ll49YWAPJ2pmtv4zMJWvCmiDl0neWOwqEwdd8o6E4X2S+kPzMLU/th4FtoXHUerz5zbOBFerIkIBNdHjOE4UvEsWEZjaJB6i1XMCq8PV3w8i5SCc005C7oqO5wFLSHWBMZFEUxgR+sRlskRUH7t4GB4lnoLQq8nls6GyVZ9Q+eRs3jurpcx9nSx6bDs8qExbN30EvaWdWrAV4vbL5R+1W/ddBRWrUFzeMqxDRtQ1rMCdW+7NsTkaJUKVBLnS+HnN35Y/A7k47XPkookNsVAuKnw5NZ55MIJXF+34io0dgakDm7knUdK/YNnYZFn9Ho/hGxI93cgD/3wyOuvjhtAUCSfuO3aEJnN6q7YrqfTsJ6xQPHdLJdJhrpSLxMGWd8Qtq3vgve7t12L14uy+4qM7n0hkbLI/qciZSn60cZAmFmYCi+C8Rf0Wa0PqF4AXyTbpi/JvofXQtkZTGEZ12F9nqJetVEwOhdQbFi05dt4FlZs0lOap1zVRsxJrBzIg4hEVOf08bwy98opqQrXGaknMheaRVRk3UJURypSlkW9Bg5RuL7WYrcWfUu5E2A29Gt6f6dwnIu75FK7W4+UxXcec6lF/XYhc5zaX0s2RaYqy/V6yDeT6PegSFnafipS1vTxLHz3Fllq+3XVcBY9VGYrQlppLlO53IoLvcWuk2ekWmgax2F9fjTSdfDDKbxO696S+7qxyTEsssOz2Pfwmu91KpxA0dYUJpYq2X0GU7WNTi0bvcVu8BRMep2WHAHrL+pC+N+KPLxHmlg6G1lllGyIf3Iw8/c+HkHvQ3ljntQ+6ztTgBPbmD56KnCROh/qdcp3xJSNXNgeVwHUId4AIr0N9Q0ejJ7t0OvUG2vkdUrcx2HJ02vmDEBwAKvBvaWvntepkhfQh8Q5JrfqAdM2O9vzOm2sIQ011NBQtCM4i34UZDn5bVla6/xky3jhzJYMotHS3vuvQbt7siYGADH7bdugh+pzd72M3Y/GWb94mlkcRHFS+kjG4rSvQtoxCCvr17Zt/UochyIerdWBMvH0cYSyK4dng/JX6wo4AA33d8TBFxw5hhnUT+Tpo6eiZFArxhtSOUqKNxaUFeJ6Cifa7q7W0JMnbrsWN374yVrfGHrO4nIo+lgcS6EviHVJKVyHdQUnUlj1BfYZANF8qiVjfX42tB/aFMud9kPD6amObHKxG73/NoCzk0MZQoo+DV3yMtCwqQCWD41h1/xslHdTUwFomHayqvxAKLu2jhyLgsqqTEviRzqzsBUmfLkMtsvybGv3ozlevPUMNp6+LhJzFGyjC9DGimB5CyBjfs/d8zfjxVvPAEBow9bvkeoErBJQY5EW5Lv6c471fy4wteJQ8cZgLHaxTx/PaiJirf5S8auKZkbaJtl3pAsdKCxeniu4hlks/p8NGd88L9Dp47ORDo397C12w5xNPth13RKKtgZHx/VMutNHTwX9k76/lQM5Jh+s16GQAUUn857qhFovDexSVXb4ojufBuEYLJoSAFDmoginRHmf1hXFXUzos2ZBbt30EpZxXeSfsvH0ddi66SVsLgiCs9S9pOJtpkj70bvlYKifbW8uTEX1A741pFCs1v0t2t3KzZ7K0JUD58LHD1TWEG+e+f/GXBY+xjCnh2dr80gYPq0L00djtKa1hqA8mYuxHATEb4bkmVw5dqsb8GJkFmke6hgPjlMxOyzHsuOnR6Jnirqq/tMJLIKWO1yJXtPDZPz0CCaWtsoNoBO1qe1WbRe5f5WL6MkYSSNnh8dZ7QhQ1nbh3kp6mtr4A3ofqDs2ATEUWbNyKyCmH1BLnXosXJvtMB7G2NPVZkJzLoBog1FFmcK1BzmoFabb6yIlHdtQJSv7RIuMhbjbjZZlbYRxK56FCE/GX8T6tViyimK+L431oeVsPZ6zFpAwQRqnsVTeEasE13nz4Pz6bXobfwqYNggOnoK32w3HOwQ9V4WaLw+wLVBWo+BsqKGGhqJw5DF0AAAgAElEQVQdLYYM4wjDTNoWbBWZ00RH4HlPcsden58N7S0FkA1qyj1ATjSRrVU/Qh1Cu7uKsacLUWfrppcws1CZVEkVEKou01ozp3dSsw2tH4g5Fo8KNrbqR0p8CzgJMZ3yVFaua2MuC4F32a8Qq6O7GtVtociKxejdclA4ryrSl4cABcy7hq9vYAxO5UoonnjpEOzzlSgRE/ELts7otEf927PfMv9njhnbVzt/g8i+c+8b2OxMYeSFVwhnkWVZF8BfAXgZQC/P8+/Lsmw3gN8E0AHQBfCjeZ7/xfnUn8Loe+KIsmcp2ZX3Us8q+8gPeUVkZaDyL/FEAX0OQHD75qKdWbimFBdikYPPRR+5aNQtJoELjWIB29D6gXob2k+rcFOXbZXr+Yz92DfmsggePX30VKHYFGTnZmcqigNhx6v9ISn2xF73xI/U9xCBkbqroR+K3yD2wFqgqJNi2Xa3Lia0u6uhvmn9TvUblD5636MnxqgY7MUy1Tlgm3YOFZ9js9x7eqZh6WJwFu/M83xFfn8EwFfzPP/nWZZ9pPx9z/lUbLXXKddflav1Wb1Psh+dyq7eRqIKP6Aep7IOr44/Jt23lw+NYezpgsNYPzkeRbC2G5yVT7UN6xPANrR+AFg/OR61DcTBb5TUaqH1cl4DyrUc266TubsRWcuN+vfo+0kpde14Ky4sMc+1GmJif6wFinWof0xqHCmOi/VF36kzFs20pmhYj7NQK5Buut44+zmi8ZlIv+aB1V5wH3fpUugs3g3g18u/fx3Aj1xIZXqK87c9SaipXj40huVDY2Gh6v12ia8vUG2rAU6rCiH78fP3zMJW+AcUHMNKqeHn81QYWiK0mv4rMwtb2P3oOF689Uyokxr2lcOzePb2uQAJ9haZt8A0UhjrZxsbc1mon7R8aKzW12dvn+sbOn5mYSuCIE8fPVU61Z2qecFqOZ0jJY8tnnzwiQJiLrDsmYUtTB895Yog6h0LiHJwf2Wp4GJaOTwbnLNaR46Fjbq1v1Nj8QNqthybbYcUOXsJ8dtjvRY6vnxozD30qKQPfS2veWOPcEXld+zNcz/xZbucxQVZQ7Is+3MAf4FC8P2VPM8/n2XZWp7nk+X9DMBf8HeKLtQakmJR9b7929r3KdemNNmk4B2aOBUtrJrAKgCRJWRmYStYSV73sbUa0ExP9MKh68miP6XJz0YLUzg0F4INeAPE7vJeSgTl0FpHjkVQaLvAPeuP6gD0Xdj3Z61PS/e8zQ2LSNd0FdM8YJ62b7+TVOoHz4UbqMLle1ydhhEk1+pZTmw/NOCQnRfWZclyx9aioW3b+pbueVsE3af7vVJrfwdfW/siNlaef0Vc1N+e5/lSlmV/E8BXsix7Rm/meZ5nWebuRlmW3Q3gbgBo47oL7EZDDTV0qemi4SyyLPs4gHUA7wfwjjzPT2VZNgvg9/M8/+/6PZviLIAY0eb91ms2F4h9xiap4T2y0F7wGpvhnM9q0p9U+Dptk3WSCuhulSpx96PjUdBhknciWhnaCwDMNoAiKvnuR8dDGQYLsrk8LLeUQoQqJ2ZRkKnkTilFpsUMKOfE+lwX7xKLYbkxm9gHiDkAkk22o+OyCl4Np5gCVaXmiH2x36ANNDwMzkLvax8szsLDmwTRzIDRthOw97w3iyzLdgEYyfP8r8q/vwLgfwfw/QBWRcG5O8/zn+1XV7/NYhjyXlS/wKeeyOKxnBo5iWU8kccTb1RcseAqZflJL956BueW2xFIilHHAdQ041HezbINWz9QKQhVtNCI4doHD3AWIQPNxqeLmn21gYi9WJdADJTiuDQIkBcVK3VA6IbhZSfXtuzf1pxp5xgwQWucqN62Xs9qVzO5O21qOZvh3rue+q1j1u/bixb2SkXKmgHw24VaAi0AX8jz/D9lWbYA4LeyLLsTwLMAfvQC2hiKsyBFL8TRFKdemF00pCL3aGzH11O1qKuec8RS9TGPBnOWfuTLh8ZwbrmNkZlNbMxVItl4ueHTxNePaApVcxmzlzF3CFDY7xX7QKvC8qExNzJXivgBcwwbc2PBZTpsTGJhGYbU1JtyF1dKzbdH7JviYwbNaeogsWWGRyqkv0HtJ4Ck67nXr2jD20ZftkvnvVnkeb4I4Cbn+ioK7uKikJqa+DvSBMsurslX1NFJAS/qJxCoMxVxAlW78QZC35HeYjdgDHqL3bDI6smL6hwEaWMuC2CrXSdz7DqZYWPuOsy/6zkAwNoDN2DlQGGeXD40h30PV8+29ncCCIyUaqMQi0ajZwEE5zv2d/5dzwEPt+KP2IhourlNyHVS8K3g7xKjMn20zt2FqGfldcbSePHWM9h7/zVhPBbXwGuMHmU5qcCSG+7vubsmMbF00I3pYM2rQMkxlNc1rQMMt8AUjWEBl745nqlTDxnAj961MZfhzJ5zmD5a4TxaRwQvYax8PcRijppwPfHPGg1GRoffXhq4d0MNNTQU7WhHMmB7Ck5L/cxSSnVgVaUHsKYqoO6t6eUN6cduejoLAJGCc/KO57H2wA3hnsb8tKepJ1YpWR2AmnW1LzZCudW3KNmIWlYnon3zQE12fmz8Tw0hQJ2LVRgqtgOIzbte37wsbEraV40mTkWwhgtQMJ+n37DfiCfKpEy9rN9z3LN1WUVvP9HJU5pecdG9+1FNMeboKjwlUZRQxrkffXiiJaZ8nZJTvWCyFENSH4j1WrXyPgFd46ezCiz1wA2YvON59D45E+kTNMCN9lG17PrBcLHZsPR8hgvizJ5zIbCLDRy8cng2svpQPNLF/eKtL2P3o+MhNib72s/93m4gqq+o0jfU+x1Ey85UbdOyc8trFHM0HoXqoLRsJWZW9cYBoSsxk/EhUoCo1Fg3S3EWiMUI3YRWRFGsYk0Uwg+xrkfD6FmyAsd2xZAdzVmcLyjLuiJbUFbqJBgmrsQgjoHX+XGqnGpPc+t0BtRPxNZHl7H02N7Il8KevmrGTAHUrPu+fpS8z49OTylmXhs/PRIpHVPu7J4VyrpL6zjZPlNC2n4+e/tcIcNLbhfdBDlPnE/Pec4CzQCEHCAWKMUNVLmIKFWDmS+bbS1lZrWgLH03KfJAWanwC3ZtaJ4Rklr2SK8kKGtHEC0ACve19wEEpZf6bKj1wFV+AhGC0370KfFoZqFaSOoDohsHrS2kdncVy4cqtOfyoTHgsb1B+Ujy0jNSYWrNm54IED1TjoXPW4ex8dMj2LrpJew6Ph4tSpSLatd83B5NoHrN24AtJmRkZjMoRDuPfDfMcbH4RzB9NOYwyFGoqKS5Ze2mG35L9CkqeHW8zE+yPj8aInMBdbRqaLdMmtwO5apntF4PAZsSpcMmI+UYPCe6TwWnBDxim+SE9P1Y6i12MTJxBQa/OfyaO10dgz3Jh5V/U+Age98DBWlZTUBMsqcp4IOS+vUFiE+51AlB6t1XfA69T87UThhvPJZ0PKlgO3aeFA7tcREp+7+HCSEQDEDgFFSMsZnPPCxLCkCmICrrFJjifDjnKu+T0/Ay0bFvCt1XzkLb0Lo4tx7uoh8GQknxIDYhNseveiJmLbOcObkpfX47oKzGGtJQQw0NRTtCDMnGRoudtvztnb6bemoZ7TBZRJ4wk4vdKG+IxV9MPvhEqE812hV+odrBVbGnsij7tV6e6uvzxf+aqT0VSyKcfsrFODJnZPX4ZDkfH13GxCdnXCBZPy5H51TZ4Qj9+NFl9D45E8YAmOCvx2N0KgFpQAwistxRkQXtu+LF+12/7yVuQVl+tlsogBGNK5yoDjelQXj53ugyD6B4z/s7Ico4gFA/n6f4yKRSVhXI+tTZMIg1h+aishZybkWMmvLR5CjxHOdorUvlM4mcDYEicviht4Uym53t5Q3ZMWJISsE5bPAbYDA6bljlpJKy7YDvq5H67ZkRPTZyGEWV+qxwUXvQXk9s0rZUn6CkCkv1vlTy/DCsGKLPt8xCUvOrVf6yD5wPbccjLzanbtS2n6zXiqK2LfXvUbLzp0pQfX/0OdGFmvLz8Hw1VKTygt/YesNz8p2lTLp8Fzp3r4hvyMWkVBZ1wF+M1j3bW+iedUDL6ofl6UM8K0s/Wd6WL/J6jEcLzOsj4OsLtJ+2j1o/4C8cS1Y3A8SbMU8+ZpVnv1JOatYHQz9ozw/EvgegsoBwLnRRe4GQrYzu4Re87+GZD+1J5w3pM/fe3LC/YWxmY01Z00LekMTzKTyR97y2qS7zJFUyq3memCFt48jZL2LruavAGtLkDWnyhjR5Q3ZO3pBGwdlQQw0NRTuas9gueUAse7+mt2iSDIW/myRDTZKhfrQjdBZNkqEmyVCTZKjql+2rHZ/dcLxD8FIkGdrRnIXdIDwFWpM3BKGNJm8Iot9arskbcpnzhlxq2o7pVHfcFDvKe6lnm7whxaJRVp3P2I+9yRvS5A3ZUWS11x5noR+ix1nYCbYfnbKj3kaiMjzQ5A3h2Jq8IQj1Rd+pM5Ymb0hDDTX0qqIdv1koy8/flu2kprpJMtQkGQLEgU2sFU2SoTpdVWIIgNrLaB05FrF6nNR2dzWwycTM632gYsEVOARUyrWUKKKJWvY9fLJmNg31HB+sVGZdY09fB2ArXCe7rVBjL8bksPWzja2bXgKW2zUIM/vKTaPziK8EjeReI7drMBmFSVPX0c8c7CorqafZHwfQsXoJsv4zC1M1MTAVHJiBYvRbSC0W3WBYtp85PlK4D1iYHpTc9hOo+4ooWatf75aDQb/Ca57y90KD+e78zSIBg/Vktch/wpHn9L7VaVBhp2U0xkNBo8GstlGe/tOINxO1XJA8q8TKgWvDCx2ZmcSu49RhjNae83QXXhs1R6MD1wJl1PD1+QoaXsTRKPKGUB/Tb6F5RAuDzg3r8Kw1w5DCuIeJ7m31VP2IVgu1Ogwar+o3Uu2kdAYpshGtvH4Cdf2C1y8SMSuXmnb8ZmEnz+7wnqbdu08Kykk5JW2CIdZBuLeKA8uHxrCv2wknhN7bmBurLRTvI1s+NIbx05Xj0+5HudCr9snFeBGu7OZh2yDbPH6a3q/jARo+/bE1dB6ZKhf2KvYV04qVw7OuZWLFpG5UIvinKOsrJ71APTo/KgaGiOGYCpzhyntvBkquT+eW5lXdnCPvYYP1AHx/C+V+KDJww6qie89VSmjHCtMTJalnaVAHMIo/0+ivoPT8drSPQGEF47oIeB8gTjVRPrc2Pxpg8lrvudHht4Adr7NoqKGGdgbtCARnyuvUuhdbiDRQnfYaPYkngI0YrfI/21KvTgXoeKCjlFs0lZQWDel5GqbQn4MiZWmMCY5VA+iyjZS4Ek7E+3pYemxvqEejftu4jfPveg69T864GAeNS5kaL9+D9p3tBjfrjy4X/7+/OuGUU9QoVjQJ773/mqGRmtoPLW+jTwGFyVnrtqd575aDAY3LcuQwrBfztz4xib33XxP1J6X38LBEOh7OIZNps15GxNKyQOGR/LqPrbmJnxe/8MbwfLu7euUhOPOts5HikqzVJFAhJI8ci36ra+6+bnHNuuWyjHefL2Tfw8VHUbyM+CO1sSE0yI7Nddp5JA5221vsYnKxi4mlg+4CXj40hvkjxd/d265FBwdr1higEj8sJJz1F9fSbQBFGLvOI6U37WNjmH/Xc1hcngZwXViAKydnw4dO0eRZ7MUM4vEXVG3mAWnpxM9kHEsvEBBQLoJy49qHCqSlliWKGTMLW4IbORvKKv5gLaFb4OamIofqaAL25OR4qNuOt40C+7D7UR4ecTsWZFaED6jrFgbpcSiOqaVO69/96CzW56trVNoq2Kxoe82N6G77NXL2Kkoy5JlOlRRZRxOllRdJ/LDti1qfHw0gowJeXUymVbLpSVpkEYsVlza3htbPf/x9Zs+5IFuPnx4J5l+Ogf2w40+RrZ9t7DqZY/z0SHR/cXka+2dWcGbPOZxbbuPccrtv3xX0poA0mmvJKYzMbIb5bXdXw1hS9SrpBjOxdBbjp0dq/fb6pCbSfspO24+NuSw5r6pv0s15GEoF64k30LSr+sZc1ve+9ovEsXtuCfx+tmNRS9GO4CxS1NrfCUqmnvkdwXnLWBQ2kjFZP7L4u05W2vvAth05hmlUJ5qKBlwYNUey8h5QKrpKVlQhymQn291VdG+rwulTiamxHNQ0xzFou170JmVX2YbWzzZYf9ggD88CuA4n9lyPG9/wAk588/pybvIwnyxr86QqsU8VBmEU55ZztLunI9OqcmreBsF2iajkvDLyeZHW0VqlJBpUrWd10nbUdGotQGr90JCBbIewdyUrNm7MZZhGXRnM8Q3yLvasWjoOnUf23XIWLKNjABDloSVtR8G5ozeLWsQlZ3fnQgXEDVtwFqn74aWV92xcTF5TkxQ/BF3IhDN7CVyA4gVXuoyYU9HcGIQ0s4/klPY9fLJm0rVzUbVR1z+tHMixMTcWRIuNuSxgL058s9gwAKD3yAyAmOuyoeO8AD1VOoMRjJ/OavoGG2CGxI2YqQ/U8W7l8CxGZs6U5a4JoojWy7nTxdpPDPFMi3bhkqvhdWu5aHVXa6KuJYXTDwLTeaQ+K944NDEUsUL8VornY11fFAXd6ddVJYY01FBDO4N2hDWkX65Tm4siFRRW41lorEFl1xlTwKJCrQUEqMQSG+OQlgtP856KDapiDPumylMFddks6tPHs1o8C5Iqy+zYgXqwX6CyWlDfoFYJtUhoGzZbOn/r9eVDY2UKxNORfO7FneRc6alntf2Wk/Icu1J5Xz28iFonLHnKRhXHbBxULQPU42R4ddpvwX6H+s44j947tWTBirY9a32zIR+eGv361ROw10awtuni1IxnJ9++IN1E7AZhIcWD0g960ZxTqQBs1Grr5ep9rN5CABAie7Mekg10ou1q23bhk9rdVfTu64XcqiRuKGoapkys5lt1cdfx9Ba7OPGZt0apAPi8mrU9qxOA0G6RCqBghLlx6nvUZz2Tt2cRIdkUjbqhAkXaP7ZjY4Xw27GpADQqvBeIWAFdHjjLG1fw/pVDxksFQDcBe3jRiqdz/YdPfW5o0+mO3yw8m/crGd3btg0MF92bH7nKh14fgf7Rvb18Ee1ukYNUF69Xv1JqTu2JzdyqnBt+pJ7Zz5sv7114pyoQb3zaN1uHDQsAVO/AbgDe4lv8whux/8f/uDYnduHogiJ+AagW9rc+MYnXfWwttBPaLDN9AbHuReecOBE+rxYmO19eWD99nvdtP7W/Ni0CNxBb/5FzX8DW4tKVg7NIkdVW8/crEd1b21ZAz6Do3uFUPy7KRQOMsQrXfmP3Fsn8kW5IvAMgaiMVDIZUwNXjcem9kFsVwBL2RgrOYUjrt2PR5ECt/Z0CY3GoKJNymFMLQIju/eATmFmom9Pb3dWKo5Q6xspYpXYTsXFW1+cLB8OZhS2XrR97egwrhwush0Ywb3dXsXVXkWapd2+3inIum1hh5boOwFroKyOIeWS55627JjH29HVYOTwe8DXEyNhxrRyexa6TRXT59sNroX1yQBrde+TM8GrLRsHZUEMNDUU7mrPYLinb5u3YXmDTVHRv0rDRvc/sOVfTIQCzWDkQZ0rXNHgaFi/oW8p6rauxQqapJwEI967a8NLsaT3zH+3hWfhwb6A4VZfK+/Pveg69hRmntko0UFFq5fBslBme72FjLgteukW7xbMzC1sB7o2HWzXRA4gBbzxJJ5YqqLYqoG2KS5LHKfRuOVgzkW7d9BImlnzxs3XkGGZQwb21HFBxL71bDqLVXcWLt57BxFKlAJ0GojCErBeow73DNSk/9vR1QQHM6OxjT4+Fsqq3ePHWM3jdx9awMTcHcjLEihT9Kuepu4rWxqssunc/ra8nV9sgrSpTDxPqjc8p2f6xrn5KOG4AmvHaKkGf+VABttKYoKRazk+pn/fVEgTEwCML9rIWCKDQKxC4Fdp14nZ4MUFUUWnfkW1LFaiEjfP6jW94IfiokKzeSMlaoNiOWru0DqvToq+R7Xs/XyX73vSaKjyt0lkte0Bl7bPKUNsen08llbJ+PgBqmdUB4GtrX8TGylWQkWxYYoCWlFxtg6kC8a5O+VZ9RkheVC6vbosWRfmxKrqOC4kwbq8N1bYXH3xenKgPV320AKcQOFbqB/zYjVX/pwCM1syDdozPlMCtpcf2Bu0+F7Nq5j10Y2rOOA69d+Mbeug9MhM2DV4fPz2HE7ger+/GzngMvKORrLjA1ZIBVIpaDTLc7hYxMzwulIuMYDDWZ61IrI/6LqDQe7BOrZd9s4da4DIl9mbYGITLYN4RO68pSBX1MRbVrNRb7GJkYnhmYUdvFva09jTdmgogbACIIwRpuH5vMwiLvDNVO+Hty7CngcfeAn6mcvWCnVnYcgO8WO31xtwcdh0fx2anwAgUi8kP9W69bD0TqTWvKjSbuTztprtUKj1pJZk+XgUOZj2TDz6BtffejJYokanA8yw6NgrY8mNjmMFWwJVwUc+/6zmc+Ob1bh3cHHUeObcaZJnKRBtvxML5rWlSLRwowwOqCZp122/EIl6nj55yrR5wsECM+rZeKiSBSrSx3JlnPSR530etbGcKI9/4eu3ZFO3ozQIYDPnWsHoWqKX39VmXWyjhvJZV9thGW0dgB404oNcBYKJkMWl9mZCyHoyYWm390KaPz9ZePNvQ+nldc6bouCwMe2LpLKaP+85Vu07mYcMAgBPfvL7MkKV5UW8O86JmO12UUZ3zcbCdXSfzwP7Pv+u5oDfBY3uBPeeiOih2eTomWm/0RGU75CQ4N3Zj17nTDai32AUOz0bvn5Yvcm+sN9Y1xX3zRAubV5VtTCydrXEbrJd/b3amahG1dAPxuD37HprgNw011NBFpx3PWQxDg9yHvfueOBIFpwXgOR95sUCtqAP4odx4j5yM5oUgqUKKcG/FLnh6gZZpO5weJqRgdU/l9BjZaU8e4jmmjyMoOm98wwtYO34D1udHMV4ihTfm0sFoUzoLvd69bU/Apqx883pMy8m+66QTdkA4A3I3gdsxOovpo+qAVYpNSAP6WkeOYeOetwXuonfLwSiEIIn1xaJXxR1ovfpOWGaQzgLCIXp5Q/rpLLw595wdR0avkoxkl5KsaFL7X7JxWTdfqzdRUScSDRwLB30ybOxHLmA1rwUxSOJjqo7AikJaP1A3vwKIAEieElBBcCxLqDbh1mvHb8DkHc+j98kZvHhrYUrc/ei4C8jS8SuxraqNClK+MZcFczAtJFZW54YBAK8vgxuxzOSDTwQRL/aArTZjKg89wFlrfyeyJEw+eKzWBuds+dAYJkqdAu/3s85ZXVqkFIeKulO1DYb3rYu6jlOTXHkUzSMAvDC8zmJHm06B4RzJgHgSLVKQpLBhfd5TnnmLHajj8a3Si/Bb71lVfOmiZp9ptgPiuAYK1fXIyrPqmMZNoAr5/13XbJpKO5AysU4snU36qZBevPUMxp6+zjXZ2bmhvoKWFpW9OefWYY8KWcV6eKZq7bP+1meA6hCwPi6E1nt6E513fYZlWK8X4lHL6dgA9G0rUnxq5vmStD3WwbANtuyTE09cWb4h/TaL8yGNaZm6bwEx6qUKxHZ0DUJjP3y7IQ3iLEg2R4n3MWuEb1VI0mnO4yyUNNmwfjTcqDbmsgCi4iaiHI9+uLrhjJ8eCfEqCaqipWTfwyeDYxaxEh7ZueVC4obBqFtFGLhY/NJ3p0A7nRsvA7q+A8/JzpZncqYbP/xkhH/wxqF1DcNZ2G8o/C3fpkd6oDCnytp7bw5cYiqaOtux8/L4Cw8O7XXaKDgbaqihoeiq0lmE3fs87nsAJqDYjek0pPqEwEEklKvqIl+HgddFGzqdkWYWtgp9QAnGIqc0aeoHELVhTzkq4SjfUgdSnNSj4HlBEUojhWlfC1h1YR7dmCtO/PV5YOOxChq+9sANtZNXRTMlRhjz5qNwSS/ehsc6U4YntF31EuQq9GRVLpHOaDxlCeWOqDTFM8hx7xYGU471C639nVCfcpTY33HDJQBIJnLmt7lWOsyl3P8JO2ebrcVu0MeE9mGg7+UYWovdqF89AOcmrpKwesBwOothg99o3Iue04bCvSkGTAu72O6uFi+hBHBZUoix1ZADVVg+spKUW9fnZ4MYoDlIdz9agLEmls7W5XBHQ671A5WFg3gAjmvlQI4Xb30Z55bzYM04s+dcbZGpiLAmyYao2FOdy9oDhdKz9f5WWGTr86OhXktLEnOBbbj1EqYu863vLkR9R7l5UAQ176172x5MH602wJUyEBKfU+qhzsbzm9LNtNVdrSmRV0q/najO8ntJ6TaAShRZKeH9k4vdMGaG9AMQtU9riIpdNfHLjs3cH7nSFJw7MW8IgKTsa30UbNQolqV+wIuNANT1JXpfNzo9mbTvVpdi6/fomQ/tCboE+/FadCIjNml/lHRz5by86Uvfxu889HfD8wwoo6TxMagXIHI05XPDnCQAojpV92MDIw0iO4+M6uXpedSHBaj0GECcEoF6hBOfeWstf4qn7LZ9YXnWCxQbFWNvKKfGeBb2m2LbNm8IULz/Gz/8ZDFf+zvbyhuyIzaLnajgBMQWLh+FNVl6cF173bOMaL39INHz9z5exCCQWB2ptmomQIclZz+s5cSFI0uf210/WZByQvQX+ZH3FKfVQ793GADcsHp2Y+DGQdMpSYPJkCza1VoYrLLXJmRK9cnWr0pDa5GiYpFYGC8AT2qsep1krT86PqsgtZaRJcGE9AvBp/0Diu+nUXA21FBDF52uCp0F4OAsHE5hIM6iMxXFmiBwhtcou0c4C1TKUU1BANRPmOg0F5mZCkTVu0wsnS1OMiL7FAlKPICZA8stoJSf1dxGHcbyoUJ8UpOszewdn9BTNYTmc3e9jI4EwaWOghzFe955FL/5p5UyzqNI5Dwepx/QPmjiZcbjXDlQ4Cx2idIydaoGMFwJ8vKC8JIDYFmOt+B4qrwqQKXYtu76VDp6nJNyZyncjGJwAqI5tHIAACAASURBVPcr9xWMxm9Dx8H8JoAv6nj+JMPSjt8shiFCrtcTH03QpB+ejX+Xf090HB3JLQeLzNMPPhFN7oRVGJVtsx4bWFjZRoox0+WmoAuiV7ZHNpkQZXosepB1y5pq/UCscFX7O+dJsR0rB3JMPljf3BQRaBd8EfClqJfRvcdPjwTsxm/+6UH8o+89hj/Ca2pseDH+2GVeRYc4e3ysZxo/PVf+XzDGHgTeXtu66SXMLFRRwz2PX+2jimXjp+cwsRTjF3rwwwV4dSl57SoVGJMzmFm4plLYm+91UFveu9J7wwO8Yxqos8iy7NcA/AMA38nz/O+U13YD+E0AHQBdAD+a5/lfZFmWAfjXAH4QwEsA7sjz/PigTuz06N5WKaoQ3e1G9075I1jSvqsSTwMBeyCvfhp3HZeldnc1KD8tAtTqVMIJZVIicMOyAYTe9KVv4yufenvQARAQppBqPWk9sNrNn34qcCw2yji5OcruFvCmZDnUZ2+fCxG4zi23a3Xzb46PCk6+g9aRY5GlSBWh3My0T1ZpaoP2WoAW54B1W2uRF12cyFYbYZx6IQXLbSe69zCcxQMAPgfgN+TaRwB8Nc/zf55l2UfK3/cAuBXAa8t/bwHwy+X/fSnfikWDyCnHQG83xbefFBRBcqJOHz0Vytj7VVg6AJgKYoRuIvx4dXFFi1BcvwN7fHg2+ujV94HPsaxuOF4Wdfad/eRHysXc7q4GsYFt2BMlpW23IfnZV8KsWe/G7XPRHNrNpyUimkdcHF/51NvxAz/zjWAlidjsQ5XSVMMNsO9s9yufejtwoA6P1xSGfE+e63qKNOHyyoEqHMAgi4pyLhb1mool4dVrcSbKxfKbmFAl9aG5WuAgpeJ95ugtdkPSb0AcHrEnev/byUg2cLPI8/xrWZZ1zOV3A3hH+fevA/h9FJvFuwH8Rl6wK09mWTaZZdlsnud9c7dlY0XnrbypC0qdbqytmf9zYlI5OdT86slyzJTNsu3uanT6pU4tsvY2EpLllAiespvXoAUHVCy3RnzyZGILG+9HloVmBO1+UOjzoY25DL/z0N8NVpKF1X2lB2u1kfaTpStwGaOZZwFYdiEyOFDhTpS8Bd+PlGOoPFvzII5ZnUtcLj2/CtTS5z1HMrXcrBy4NjwbuNugD4vDBb4S8SxmZAM4DYBRXecBPC/lXiivNdRQQ1c4XbCCM8/zPMuybYM1siy7G8DdANDGdb7iBtWpGxSJ8BU6tWflmp7w/Vi41PNWKeXpBbx6VXl6ocRTambhuyE8HOdsO230U7AFRGNZ766Ts8myg+qwbW7MZVhY3QcAODT1LE7geoGcD6ai34PPNsau1KziaiWaNOU35rKAYuXz+r9Su7salKsBMFXeq77fSvmqkc75vMeppbhBVczz+9cIYBSbLWdFLpQRvlJjBbaXGPl8N4tlihdZls0C+E55fQnADVLu+vJajfI8/zyAzwMFKMsrY0PieTE41XTqKYmsdr+32C0gsg74alNkQ9XGK7vHdmzofn4Ikbu08VTULFoqsigsVz9yQDJ7l4o3xr4EEIFzNjtTUf2sw4pCXjg9Ha+Gc1MFHBM56fV2N2Zzlxw3d/VyZfCcE7ge73nnUTzxyJujRUKLkJJ6yHKj25gr43SWGcN1A6DlyG6gRNiq6LfZmYqAZVRaKkzeEvugoqT9nlhu66bNsr/X1cRGjmNiCbU5AGIglrodqJ6JMHC73EMgYlTiyURnqnZQbHamCt5/SBoKwVnqLP6jWEP+JYBVUXDuzvP8Z7Ms+yEAH0RhDXkLgH+T5/mbB9XfpC9s0hdq3zzEplWwNukLX/n0hcOYTh9CocycBrAM4BcA/A6A3wKwF8CzKEynL5am088B+PsoTKfvy/P8Dwd1Yif6hth4C2xLn9XnNe8FUEGMbRbxlFnYWkMsKdRak/cCiNroZ18HgN59vbAZ2CRDNtfE/LueC/k67Gajpr/UeO3GrP3XDQNAlMHdgzxzsQDA3vuv6Wtq9GJ92PIKg2f/GKPDOyT4bTDJEMtxc7KHj2ZtV3O/t6FbTthGa+McapZ5zgk3C/Vheu6ul/G6j63VfEN6i10sfuGN4fl2d3VbviHDWEPek7hVc+YorSA/NUzDDTXU0JVFO9qRrF9EKhL1DQrUslGqPKi4dcKyZbUPKbwCn9dyFqwF+OIGUHeAAqpTThP6jMxs1thX1key4C3b3xgRWefUkkmGPrRnqCRD2p5yhp7exOtf774eep+skgxpv9T7Veth/3XOyO2kkgzpGD3xhnXr2PRZHUvQg8j35tWp15UsiFD7oO9GHQW9789bF/welDP2nCBfdWH1vJBlnpLTLmygHqKNNBBfb8QGj3XUjQxA7cURdce/7cekohBRiso+283RpuzzEKvewkiR1ZlE2dJKb0u7KAFEKEcgjnNJsv2hvmTtgRsiZSYXv42V6emUbE4OzqHOq3Ux90Le2fgW+g1EwWNKRKciIr0NwBM9tZ1UCEYPzOUdhJ4XtTu/zkF4Vbmobyf4jZ1Qq+yyz2obnmLNLnbvmg22Oigeo5rxSGqhePHWMwCqRLtcmFYPwL+1Tvu3AtCs3sae+pSxvRORG0O4Vv621zVmKH+zTUt2rqiwnbzjeaw9cEPNauPNa8r6Y+eXOWO1r/0Uz+yfbpI2IjvLAPGJ7YEBtc+R1cnZWLxUial8pkB/jtt+e7ae3mIXT41+fWgX9R2/WQxD58NZpEgnNxWM1XvG+rCEts1JohyHjWcBoPaB8pqKEyl21ppq+0Uo90yonr1fY0zYjGRU7AIxF2E18Ryjkh2HeltO3vF8UMICBcZEI34HfIH4o5A4V55iOkLPyomc2mhJyvnpRt3PUqRz7nEmyh15AXu9g8vWpeOw8+xxjd6mc1EVnFcCeaYmBap491PacV2UNkzd+nyRUMZyMZpkiDiBcNqIeLAxl4X0eMo6KhaBH6nNPWpxEtGLL9vQ+tnGUrnpUIxhwJaNucJLdGSm4GTw6HhN1l45PIvOI8W1ic5USCk4fTLHyoFzaHdXMTJToBwYM3P6aPVeVIxJ6SsiXUpZVlMlqmk1LBgg4AuKd1b1l+90GjFZnY6GqqvVX9bJTW8SdR8O9jllTtZylV9GDO33Dh6N9al+UVZXApRRucrnQggCpJ0I1X2dZbYDymqC3zTUUEND0Y4XQ4YVMTxuQe+TPFHEUxT1U/5ZnYnWY8vpdUVDei7sWpYgGroZq+uzTXjD57R+IAacWTHH4hhYt82iTldrzaJulY9Axapb+T0FLKtlUZc5UZm99dFlnPjm9TWAG+fX6ies6ztQ6WkUmKQcgZdF3T5vORObu8PqrazVqh/GwguVOEipzb7buLF2fqyOxJY9/o1/NbTOYkeLIXah83cKcBNYTGMOjVhEFC/Iso+t0p+ALG0/qwHrUVJWE6iCu6i7ND92umcvl+7Zk46Siguy8C8YCXk2p48XL18jW7ENrZ9jsIF/lAg3Jk0fz2oL+8Y3vIATuL6Aape+DrtOVtBzkr6bWO6fqsn17KO2dWbPuVCf+lSc+Ob1uPENL6D3yEwdwdmNA/QAwMRScU19KNiOwp2DgrRbz6JOfZHOw8TS2Ugpyyzqdsxav/aLcPmaDgzx4cbv1SpgrTmVZVuLXXcTevb2OcxgKiiZN+bGMH/EMf1PXFt7NkU7grNogt/USfveBL9pgt+w7p0e/OayET0IAYRkMvytsSEZG2BauIlWdzWcbPxImdgG8tKWD80VTj6Ig9/w9I+xC7IIxclnojNVOiPNRqf6+nxpdiyVfrqQrQVlUPAbLqrx0wCQh/oBhDbs6W1ZVqCANDPYiw1+M356JKp366aX0H54LZS1m48X/EbHy8Xx0O8dLgPX8CMfCfO7dVfpBvZw/+A3D/3eYbznnUXFXzn+9ig1I+dMuQFrIeGitDEiZha20N1T8IR0buN7rzAkxbfVQ8FpAUDvkZkwB3pIkLO58Q09rB2/IXqHQD2XKblDVd5a0z7rn1iqwgMCxTdhFbkIfWzVuCP2i30fdGBZahScDTXU0FC0I8SQi6XgJPVTQFp2r5+Dj603tOnYq61MmUIDAnWgkYfTsOKWddiKooibNjzAl9cHT2Fo+57CCAA+CE3ZZEbhVtdyJevOruKNpyQl/cDPfCMSSSxew46booKnzLVJp9lvZdXJuqvIoXWpQ5eKpUqeuMA2CWYjWci4/T5S3zDnkO/FInftN7rZmcKxr/4L/GX+4pUPyhrWN6QfUq2m4DQaZNUh2I/fazelD/DKedpq2x6QTgxDd2kvZL32wRsP/+4H//X0C9ZHxNNxpLwoPcsEUPeS1XuedcK+M4ugfOZDe/Cedx4NehBP32HxIh6GhNgWq4u5+dNP4Y/+4WuisqqXsqRZ3jzPaI7XigXW4mL7b9+d6md43UNl0gvWWnXUu5rPP/XHv4SNleev/M0CGA7uDfgOVnYBD8wbIpSC2PJU9xannjApjofXrKOSl9+D9+yYvMS/tn72R82RQAz31g//2dvnaqnwdMzqm8GP9MRn3hpS4ekHbZV8nkJV+6ttsB8k7/0SZs4gwFbhp4o9oFL6WW5BUafaB2tmvvnTT+Ern3q7a6FIjclueJwX6+JuOUcdB8keBp77gnLgqmjW+vlbDQGNI9lFgHsrS2zr8Z6xnMYwHIjV+gPVR8KTIQU59zYhW79tA4j9ZuyYLGfg+TzYevRZz1IF+B6xis/w+mEXnRUXuPn9yHu+HqKGA37SZqAei4RjtX4YQAW1t2KTjZnC+qyDIBexFUVSpI5zapnTTUHny8N0kNSp0Ipn3kH5tbUvDs1Z7GhryLDkxe8cdN9e48tpy32FcQOx7KgbkOIrQr3OBpISpYDqY/bMhpGOIrFJDKp/ELFd+/FZPQRQBtvpdmoLeFLKA0W8yImls8UCAiII/iRia4pujsXYqjkApoyoNhWSMGnU8N956O9ifX4UWze9hDN7qrdCjmL50FhIPcBsbNNH61zmvodP4pkP7QnxKnedzPEDP/MNPPHTb47eL+tT3Qth5L3FbkgOlMJhsK5a4qpbDuLEbdfi9Z8tLxw5VisDxH5EIQoWEMIK2jZaR47VrFcjZ4dnFhprSEMNNTQUXRWcxaUgj53uR2onByqHoUhcMOUtQEeJJzn1DqoktUpH27Zer1leyqC4DGhrAwTreDcFc6I0fnoEm52pmnxPXca+bqeYg/JUs7EuSa3uaoRraZUn6Pr8aI2F10zhdq4ohlAkGXv6OjeKuYW/TyydLeeymgs6DxaetnEbM6jeA9+Lh21Zl/nlOC2tHJ7FpOifrCjx+m4shpDoHMjxbHamYjd90xf9Hi2H4/WrH12Vm8WFiCXKknq6gZ4pz7/tQiULaK/b+0AR3LSuQyg3k8Ozrvyrz/drI4KEAxEsHIhl+ZXDs8GrVkl9JorfVZ38OIlw1GcVLKVE8cqDwnsw6YklD3BVbFYTsiFSJCnMqhV6knI84d2hf0eOYdd8jL78gZ95Ck8svLmmR8Dh2QixOv+u57CEvaE+RfTazay32A0iWAW6Q0i6/cyH9lSKYlTiEAFXrW6s1Ax+M45+IoDPTPT51pFj4W8FNGbffgnD0lWh4LyUZJWl/coBdYyCUj8HN5KVbb0I0P2Up16f+pUJ1wdE4LIxNajIsyhUD1eiGxv7ZZV4a5Lx3dP4K9lAzIRrjz19Hc7sOYf3vPNowGGoNcbqXixcvLW/E8ygaj1hsOC9918Txrt0z9sAwEVJ2nnup7NQMyevU++j0bitVZBzE9owUbCs1dAL/NTav71IWVclZ3GxydqxPbLX7YuxdVlzVyAxdU0snRVIdnx9UP22Xs8JL+o/qk0gcCr7qzgZG3NV3pCNuSwo3CL8RnlSWlAYIfakIAaZ3BjkEtbnq1N1qXTAsr4a7W7lLEZlJhMRKTR8YXUfWg+3CpPsu57D2gMxBHt9fjaMYW1+FOOnM7S7q3jPO7+Nh0DgV44ze9pod09HqTNZnyemRqIDUNuIVUTwklhpDthwrXwnpJZwvh5uyMXfGGDiyGgTz6Khhhq6yNRsFgMo5b34StdX6B+KE8HLlHWh9W+X+vVhEPWDoF8o7TpZQMwXVvdhYXUfDk09G+4tLk+HbGapvvCUZ6rFfu2wvguZ881Sh1RwVNX4berDFG3HGWy7jmOWdoTOoslINjgjmb1+qTKSPXfXyyFY8MXMSKbE+5qIR+/ZOqy1SH09rI+LDWIEFC7yf/QPX1PDoTDDmLVEaBoH7b/CwK1C+pkP7QGAKFao0rc+MYn9MyshTKAVF9RnxEs4xZQMrfe3QtsnPvPW4LMS+i/9UX1N97ZrceOHn6yB3449/xn85X9dvjoQnNuJ7u3FrbBKNBv3QtvwMqApFp9lbRwHGyMjdZ31BW23QQNqdO+9918TwbX7+awoxHcQwo8bkPVVYBYrTzFnFyR/28C/dMDSeBhUDFoiNF77qrBszyHM6kIshJvjU2AYUEDcGVNDyXIVFnZv86TYDdPmYgF8hK32zTuk+BxQpSqgkxuQ9h0ipXyDrOOb3Yh7i6/C6N6XklJh+YZ57nwUnJ6Phff3+So4U6Sbo/WnUYcwAC5E2sKoSZ7IoeY9tmURnAp7txYHtqc+MAAixykG/F1cnsb+H/9jrL33Ztz78V/BPR//gFs/+0orhKZ5ZL3z9z4erCBn9pzDr/7w53HPxz9Q4/zYJy86u+eroWZpjks3Qm9T0bI2CBIV1Z43qo4dAB5/4cGrI6ze+dIgc+cw1o2Uf0XLcCup55SrUf+KNS6UxSq0X0vAOdRNR67o7705cEPTic2GH8aabCxeX/ttINNHTwW4cLgGBOhwNQ+jtTii6xIyTrOSe9yZQr3Z1szCVCm6iQUFiMyHSee80nLQ7q5iX7fY4Gj1GJvLwnP3fPwDuPfjvwIA+ODnfxIzC1u1nCbETaw9cAN2IT5IW/s7UX/uOV7U94vvf18ow01gXzc2o1siBL6NOEQfQK6oCjNIrAzHaLkWdanvLZaRzQOeo9po3PAJ28ii3ig4G2qooaFox4shl8tFPSV38qRMuajbtPap4DcahyHFNqo7umUnhwl+Q3HBCzGXEiWsSKTss1Wkqd6FSjpluVMiRKjDJFrieL14JJ6Lug1SY9lxJRsj9XN3/7sgQqSeVz0CdRY2LJ7GagUKxSL1DZ4ug4pQIjY9BKaXV9VzDdB5sUhcq5TWe+qS/63f/BdXh87iSgp+Y9vSF+al1wPicPL9FGKaJMgqBFmu3wfC8kA6MbJaiRTm7C14rdNz2R52rnRztRu2vjMtR7K6BpJnDdHUjKozsCIEv42le97mRq6iEhiIRT9VXAJ+usY3fenbAVUKIKRc0I0VqNzgrdi1fKhICsVNBogDFOu8trurkc4FKFCumlwbKL6/b/+fn8bWc1dJ8JsrhYaJmXEhcO9hc1B4fRqGBpk+7SawHZu9VV6qJcZyLh68W+sAYg5AOTQb9AaIN2gvb8jP3/fvg9JT55m/2V4q+hXd2TUXrRdPwz7/4q1ngsXrzJ5zbhJpa9Ui6SZs871qRC97oHzrE5PY/ej4la3gzMZG0Xu7n0jlSksFQG25p9mPcp0mFlud9az+B6oQbp4WvBYzc8Cis+2y7ijd4kVIBcCI4lR8jp8uRIiJpepE3ffwSaCMBaHvn5vl1k0vhSjcnUeqfkx0piIlqfaFxPo8T1RVet755btD3Ww7zG35zahpGyjuM+w+aWZhK0TEAoDdj46HfgQu6NFxLB/KogRIAEIMDADRe9iQMHkcx/r8bHiX7BcwixdvPYPpowxdWClJdz86Xpar6j33S8NvAY2Cs6GGGhqKdgRnkaImbwikn1XeEJ7eV0rekHPL7cBVkKaPFw5bw+YNObe8Z2DekGCW3t8JcTp0nFaZWXBAU7jzy3cD6JM3pHQX7wHYP7MCAOhhxlXckhPbP9OrOa2xXfaJWeba3dWQ0FhN3ax/+milE6nlDSkjfe1+tAJfTc6sAE7ekOmjpzB5Rw+9pZlQ7/O1EaSp0VkkyFOQAsPhMrTsIOQdn/WsO6p09WTlfvqRVFuD8mKm4m4qcIjkiSGeviEVD9KKC1bE1HZTwDQVxzbmshDPQrOBcUwK0+dYbfyJmz/9FJ746TdH7L5iXsJ4P7qMpcf2BjGiXzyLfnFcCcBSxaW1NAH++xsmnoWHxdENq0kF0KQCCH/bj8wiLZtUAE0qgKsmFcDlpgbuvXPg3jaeBRejIkY35rIQAAco41m8Px3PwsM40JdEExmtHMijDPPLh8ZCfd4GYq1anvI+hZ3guD306jDt9DsUtY3t+obsaJ3F+ZLqAFL3dcKA2CGNRN2HaqXn7+0OxfFovfpCKUtPQ160vERaMIoM67Fugn2YWKpvBKxLZfVpVB9iyyxmBWcBxcm2fGgOkzIGau8nluJTcNfJPJghK2/LagzMDq+WFEsaDZxlgUq/oNYF21eG9aMvB6Nwb8wVfZtZ+G4IXAMAr8dp7DqZY+mxvQHCzQWlm5CG0tMAOk888mZszI2FfnMOtD6ds3bQl5XvwdlMlLwwfDMLU66YqZaTfQ+fDNwfXdo7jxTvVyHipOpdlO8KAF74et++KTXWkIYaamgo2vFiSJORrMlIxjFZuHeTkazJSHbBNIzXqYd29Dw5lXSiU+X6BZpNKUs9olLPBpVV4NWg+r02UhnJrIKRZXu3HPSTDDlJhbmJhiRDZf9TIqH2g4pcuzkq4M6zFNECAiBsHC/eegbnlhNJhkpSJa49OKaPngrBYzgOJhnSQ8uD61vxlvV6ugXPwsH5Z+Jm1mVBiToPVr/BOm0b9j209m8vYO+O2CyaSFlNpCy9Z+toImU1kbICpTYLL3KVNUtRyWXzUOpC63cCKKzbg4Grcs5blHxe2Vqgss3zFCaloOzeB6KkCj9ritQ2UtyKLlDCj63PifUzePHWM5Hzkd1YPC5ET0Td2JTGT4+EMXzrE4VKlVG6UuIbFwuAYIHQxcZ51L7o8xTxFGfBOSRUmovZ4jeUs/mnX/kygAImTsuJio/kpn7+vn+PD37+JwHEZtGUZ6t9T+wj26dI2frocrDoWL8YoNjgb/70U1h44zVRcic1Cz/x028ObfzhU58bmrNoFJwNNdTQULQjOIudiLNoEJznh+D09BMW1XihCE721+vj/Luei1yzFcEZZf4q+2r71vrocsBlKIKT+iqKC/d+/Fdw55fvxo0ffrIW96OfspKkSmgANcV4ymVfvwOPMwkciMOp2jb3PXzyyvM63YnExZVKDZgqT/IAV/a6VybWjhc4B6tItIs2Bb7yrqe8XVOafQVl2aA60UKnx2ipiScVOA0/+A3bAEpxDgcrHAoX2KG52kZh+6wWpCUUG4XG4Jx8sFhkv/rDv4J7jksMzv2dyDNzYulsAeHGyWjTmSjFjPkjFfL0zi/fXcTgPP6BWt9UNGY/UyL2DIq6w6Zbigv0ytU5Yt2WdL4pRjJ0oYrdLOfF2xiGrsrNYhhrCKmJwVlRE4OzicHZjxoxZAAN2ngudX2eSbcfQnU7G6XSIJOvPuthPUgeWhPwWWag7t+S8nVQvwwiFpkywEbr0nq9U13LaOAallEl7aA6gCKADjcMVax7sHfWQ2vW5B3PY+mxvRGuxjOXa5/7KUXtpmqxPVZ8vWpMp9u1hqRCz6lcaE8nlfGUbfOcrKy9G6jwAfakBYodf/z0SHR6eSdkP7OkZ0akt2QVTGZkoB+BjjfFhlLk0ETD1rTs9VH/VrOy1mtBRnR2ItGq4I2Z754iwPy9j7sgMMA3ndoNgcS+aj9Ytwd6I8iM/dX7P3/fvwcA/LPXvKGGTSFuY2RmMzIVq+6GuhCWt/P9T7/yZbz/D34CY09fF9pOmYT1PdKaRX2NmoU3O1N4fPGz2FpcaqwhDTXU0MWjHcFZXKiLuuUKLMrNg4p7ikYPocnrpH4sumc5IQsNpHN8elgBq/32ArcMgqx7/VXOaVCE755RVvbLvNUPzKTtKSnuRRGgHgzai8LtBbb1OELbD6Di+tSN2yJIPU7K9se+i5/79jdDTE8SxQovO5zCs9muBeKplUoVzvP3Ph44EusZrNHHOTYdP59Z+sy9V0d07yuJrGWjnydq6j7QHwIO9Edppiwvw9AgBKYHyBqmD0DsN6K0nYC9qTb1uhe3YlAdNn5Hv7GxDo5FXeV186DSk6kIAdTSLDJ4DoBow/ZEUhusmOKnrVPr0L6S7IbVOnJsW74hA60hWZb9GoB/AOA7eZ7/nfLaxwG8H8D/Vxb73/I8/93y3s8BuBPAywD+lzzP//PANhIBe68kBOcJIjhNlGVFVyr304OP4EyZaoNcfWguieC0GAUNeXchCE67iDx4e0BwmkjX7e4qToRFU+lYGLawQnAWliDvQ293Vwu48huKEaUQnBOcW3mP2t/lQ2PB2kE9yq6TeXDu2j/TAz6J0L6H4KRu4p6PfyDUp9/YrpM5fvH97xuM4HzgBuw7mk4BwT4CiHRik3c8D5SWHtU/KQfxpi99GwtvvAbLZiMEpvCmL30bT/x0OU+3HASeii1V/WggZ5Fl2d8DsA7gN8xmsZ7n+f9hyn4vgIcAvBnAHID/B8Dr8jx/GX3oQjmLFKah330vqM0wEbG0vFdPv+f6nd6DytpNa1D9/frgkW6unkJz2OjeXpv8kBlzAUBN2ecpFIHqBE+F19eES4Pg3mphUCU44d67Hx2P/E08cUfzewAIymYlijifu/vfAWDU8DhvKTdJbZNtkdQHhH5GKs7pIXfiM28tnnc8aYF6mgPSM7/96YsH987z/GsAXhymMgDvBvDFPM+38jz/cwAnUGwcDTXU0BVOFwLK+mCWZT8B4A8B/Eye538BYB7Ak1LmhfLaFUdq/1cahFOwYoQ+zwji/L+fO7yy/IBz4jjAMq3fth0QnKkIYvs7NQWcKoqt0jDcU4XwkWPYdABINiwe72YmXQAAFnpJREFUAGzMXQeggiVDFMQqWhCEtmt+tnZaBhFI0KkBDOcMcWLpbISUJFEsC4rKklOIxNqyrDWZsj4tu3LgWux7+CTu3FNEDf/VH/48/uVnfzjqy7nlNkZmNkPkM3JtkQhVjm9kZhPTRwsTaLuLIOoGdwBgqIzr3dvqJuSRs95M+XS+m8UvA/hFFELoLwL4FID/aTsVZFl2N4C7AaCN6/zODWsNIeudsoZw4Ys1xGIc2qjjAbx+hOecMtpWYOkRs4Jt1tGZwlLQpaTh1l76Qmv9aJv2Ql1G+QcUiEyOw+qE2t3Vgt2fr8qm0hcGoJTOi6MIBaqFpEhEQqj3dTvRfNn5VTARqcpZOhq1AyAgY7UfhSt8fSEVLue1acfGXBbC9AMV9F7TF3IeCpoKnrXTx7OQqIgL+F9+9ocjnERxbwvANeCGGawaEqCZG+brup2A03jdx9aiwMWdR84Wa6Msu0n911evx/oDlcgyeV8PN37/kwVqVvRym1+PQwT0o/PaLPI8X+bfWZbdB+A/lj+XANwgRa8vr3l1fB7A54EClOUpOFXh1bvlYPg9MJ6FLCYrw3Nj4EKdQfWsLgjWafH1AXjUjctZfUGhsa78O/i8/s9FqWTnIASQsQ5moX6ENmz9SuyDnt6MxahzUwCyig96ojMVpRm0G5o11XqKUDtHOo9bd01is8wiZt+n1rlrfrbG8ew6WUdJpmj/zApa3fhTb3dX8dDvHcbrcbq22XgpEFnPZqeKR6E0MrMJAJg+WsTHsHP1/j/4Cdz3lt/AL97/PnhUzeVoqFsPqZGZzeL5zvvC/ZGZTbS7axEXxYPyW8uTGJOYouG+cIPT+zvYPf6S2x+PhjKdZlnWAfAfRcE5m+f5qfLvDwN4S57nP5Zl2d8G8AVUCs6vAnjthSg4X21h9WwEa6us7BcNy2402wmrZz1Obf12znqLRZoCHa9naaLlpp/Wn23whFVfEvv+SAxRRwWh1um1Zb1madbUAEUeFgIAnrvr5aCMTeEwUmZKEq1mtKZYfxLFSViPXPsdDPIgVgyNhZxbxOt24lkMYzp9CMA7AExnWfYCgF8A8I4sy96IQgzpAvgAAOR5/l+yLPstAH+KgjH4qUEbRUMNNXRlUAPKukjUgLLSpKkUlKzfyXaympEs9iblVGZNp0AxPxZ3kuJMrO5Hnb0Y3i7UW0azokn23HK7BqBSHIZyACpO2vikJIs4VfOt4k/sOBi0WOu/qKCsK4EuBc5Cr/era9AmYdv0XNf1b4Wbe2W9vlrxYJjxWmodOQbs7wRlHhDjKSBiBn975C00lcdJG3NZlN9Cg+dsOo5cFv9Bpef6/GwY52RpJVKLEMsXepIYqLbZmcLe+4HlQ8QmFPV7zna0UuhmRJd+uuIXdZzCs4/txb6jJ/HirQWWotBnxIrED37+J/G5u/8d7vzy3ZGo4IkZZ/aci5zOqBTmvFhRLSX2MZdt2PA6U9sCZV0Vm8Ugl28XgjzktX7xIIb5be9pfdupV6/3bjkYlFT96vfq67dp9Ba7IXYFqd1dDXEngkkW2+MuqtO/+oAnXLV31ab2idYebixcXHZBBCW4XNN+qgkUKMyn1kvWW2SbnSm0Frs1/dQMivom73g+BOIt5nkOm50p7H60ChYEbNXqZQCdD57+ycA5sI/aD9WrWJRuKON4qtqxk0JdR44BE7WqknRVbBaXki5mLIvt1GcXvZoUW91VF0cwTP2DYl20zEJN9ad15Jjfh76cRUGpFIxBie20OyH1qjizMZdhQhSBRVTrucihipsoFzj7MH30FM7sORctsMINvS72hA1a+sH6lh8bw/yRx0M5za5W/F+NjeICRYgPnv5J/Mk/+bf4/v/xztCHCBZu5gb7O9FmunJ4Fs/dNRlMsnS5r0Sbamxbd03iWUhk+lsOYuQbX8ew1OgsBtCwwWqG1Vl4YsUwNIxIYTmLYcQqrXOQeOO1nxKBLKX0AF5fUxyetbZ4HrkW6u15ifI3IeQKn/bigCr8W09leoumAt1oWwpbB2LvUAD46n/4VQDAD739R8J8KVfgpWQAKpFJYegp72bb1425DM//0qea6N4NNdTQxaUdz1kMg7OwcSOIF7AnLSM5W8iz9TQFqt3cxo+kEs5miLKejt511mfDxPGk8PAQKdg5x857POUUK+Epu6jgYhJgnkLEiHgYDs+xTE879t1mGuc1RtS28+hZXTwnLipZLZI2Fc3MGwND5WnfWL/9lmzfdB5TfSZ5CGBiUmxKRc/69H9/43fw2t+/A3vvv6YW44LPkZTbsONXt3agwplY0e/x3U9iY+X5V4815Goi9cyktYAKtotBlfwff2hnbtsT7itR2VahPqvf6/Oz4dr6/CzGT49Ei2djLsPIzJm+fQl/lxtqBbU2QWKkvIbv181YRQhdQK0jx4Db3hqsKCSNR8m61K8k3Ds8G1mFSAquU9GotoBLPYYHrtL5BYDX/v4d+LN3PIDX4g6MPV1aaVBtQjYUYW+xixUJOsSNku15mfciV4hGZ3HxyJ4y56ugtPUNMnHaE105KwC1E8Lrr1dvP52FPcGVYwFiWdyL7WHL85mk85qQpu8DYoWgrcOLsQkg5F9VBeiZPedC2j7NjGbT/mk/6HuisSAYw1K5No1i7lkjSB6HqFyMRVU+d9fL+LN3PBApPUneZnHiM291Y4yyn0A6d8nxb/yrKytSVr9cp3aRWAWUx8pxIXo+CXZBeAsEqIAw3j3LDitGYOmetwGo8jd40aBYh9Zn2WMCbVSk4jywT5GDmmIiOHeO0nAQ6Mkq0FSc8jYy+7zdSLzExrYufTf2b/v+eX8QXiU1Pi9uhJ3HfopiKw5oOED2jeU0zoYX+tETf7nZzSxsBaXn3/nX/3PgDKz46m3kTEJlEyavvffmIH7yme1sFo2Cs6GGGhqKdgRn0UTK8uvVE86erB55nNSwpG3Y56zSr9+p7r0DzXhO4unZr04dk6cQtikTrfKblKoXqCuVeS/FUaYcA5Wj4NjorEbXciXmPmFkq1TIRgD4k3/ybyOTqoaJLNzUv+uaZNV5rt1djaDtfAfbCdi7IxScFxqDc4l5Q8wCJutJd/T5e+vsb8gbUj7rtU2KlGllecq2XoYumzdElVraVrTA5V5b/tc4Ga0jXTdviK1fScfr5Q1p7e9grVw4W3cVMOWQN6Rs3/axd8vBKFZIq7uKpXve5tZPuDXncbrU2DObGGN6WIWfzg0XD3N7BHlfNlLPmlHkDan3h2z6Rlnvs7fPFV60nalYhCvrP5HIGwIgZFf/Z695AybKvCHTx5k3ZK2WN0Tzt2o8C83sxoX+Q2//EYmHUb2/N33p28A/LMZMCDhQfCPLh8awddNLaD8seUN+PM4bMtGZwrfnYhh6P9oRm0WKLLiEv208ixCTwsiWlEGJ2lM9hjrj8Nn4xK9OEIueLOJZdKLnq7gYRdluGcB3Ymkr2hT0I/TS01kiCo/9oi/AyoFcTqWt0PdBYC81p1kOZ2LpLF689eWABlQU4LBcik0twHrZHlBtNls3vRQWTz8HOi4qhYhbpWA/uvENL8D71Gl5sn1WnxVbT++RGbeN9//BTwAAXre/iC9h6wzxKCSehfbfArmAmMvw4mH85p8exOtQbAbKTU0DePETBapz5fB42ffni3tiKm4DFz+exaWmi5WR7HJG9+bmQPKie+vYgFh55+UrUVIteCq6t61fiX0+n+jeOqc6DquIZCYujXFBjIGSBtSponuvuYpP9v3Z2+dCprS9918TKV5JVgluyYoLVB5zbrl5eUr0ICbdV0UYV+V1LZDvHc+HfKv2u+TfXj9VzPBEiq/+h1/Fa3//DgAF50cuSzeZyTueD5ngSUHReV8v9Gti6ey24lnsiM3iYptO++UC5f2UKcuTl3XzsgFiUhr4lJxLsgvv/2/v6kLjOq7wt7Vsr+otbK0tslfEWhRT3BLstK4SEjdQLIop2I8mEPxQsAtNoBTShz6kj/JDH1oIggbqBNTYFIyhGNxg2uI8xTGpU5NAqVvXXlaq5UiNHESQXCndZvsw98z95tyZu9eObW2k+V6k3Z07f/femTPnfOcc33ehYCwAMlaPUBtsjZCxM/FJk8BCJkpdv8yFLASyk2rCGuDXWQDwWhGkLS35aWnSpzOShTuUL1WPgSnbgGu25UVEyGpiURDooDvSB9FFcEAbnZRYjmv6GQPgbG5Sp/x/bbyKf35nEoA5nshcyULK8US1fkPnoY3WkIiIiAeCnpAsYmLkmBh5NRMjc3Cee02M/NQv/wwAuPz4Bnsc06QunStFoKVXH8HrG7+7gdN/2+skRn7jrbMYO3I0wzGa+enThoy2+yb6fmB0NdfGq/jqzxZSJS7Me3Jxw+v4+B9z6/cYshbRzTzcywj5m9zNWEKerr56tb7D9z2QzffKR9OQ70roWJZH9/YR3rRfjfZ8BVJWK+uktBn9wqnXMHbkaNDr1SGGvTSH9vFBp+zdJBnqaWvIveJ+6Cx8HIc8nUW3OBECH4OPH2if0xsHuwmNzacY9O2yms/g4yzkMS0ZoksA3BR/2h8hw3totjKxMHyMW24nRF2XXRxwWbMCy7AFsOihaNeQQnQSNQBtuKZaG5xYWbPkRWY/Eq4PMNaJtow5GRs/R9PHqg4PYz6RGmtXzOfGuf86DFwe79iRo7hw6jU8etqE6BPKu/WdoWdsfvIRYMhVuD6MvCFrHvbhBYKBZnR5RjcFZ99Iw3IUnBc8qUcUgrXEsYl3E71g5LWxkJg+WcG5MLQJlcaA5VUAwHAr60DFPAqNxaFN6HuzZTkKlZm0vGjxZZHSmb4FfJzoa912FjVWcHIdMjdiJm4jPRKwMldHvpbPOhamm77wKWehEdNq30jD+GHAzSnK9cl9k6RBRtG5zZbRjoAcM3Pz+1/E1OE0eE3t4geoXVRHx5ZZ4BeHNtljIs6Y+/Do6R/ixrMmVeKTV57HR9/7DyoqGnm5ZSQUE+LPVXQWRVRwRkREFMKalCy6eTn6QsLZ3ZQkAmYRmr9wXKX52jwyVDdqt6QdbCPdoZjx6BPNi7ZhTXb0XRruzSU6dYPbhumPNpm2my3LpJU2K4mC0kfYYsg8MpFO91n6Ye8THWmknaruayLFlVtZic/MT3qUEompDKBxbsD2S66RMYiiWN83kapY2V1u3U4zhSUQnsxyY8BRsvKzJNfrREFiHm033wP2bceuiVk8eeV5AMA7P3/FKj2F6Sv17pow0sqFU68AAA5cPYjO68WIbUCPLBafle79IEhZCyFSltLUy/XXNSkL6E7KIop5t3gVHIdhy60OauR1WoiUlfxtJol5ZDwspoq465Kywn3i2A8CdpeW+3D90EYAHcx/0/hK9M9+wY6hecyklclrBzCWCMPEBOYnH0ENfkWpZouasaXBekRHIRYaPoZUB+dROT5oFxLLv6AXXawel158wknfyM+JWC/+9Itvp3OV6HMWrHJxwD6v7A7Pmw7zNKQtJnuJJY7HIDqMsSNHMT1exdbzsPVOHa478T4BoPMlf+pQH3raGlKE/ORTimkXdG3e005TPmelULtFHMUAVwPucyUH4F3EGGKKzDMLS7mQizn3B0Bw8WXTp+xkwur05TqVOrvFcZDvfPPIZlytmOR75jP3cj+LmE4lfoUTmuClOWtaFIjpkduTvrUObcxE/ZL6fEpd5/k90UZzruYE1g0RsthczXOwsueOowiVcTGkPlF6SlrFZ7/+F1x68YnM3L7dnMBKc+bzYw0JSRYL9DCLYs4nWXR7oVirjZGGsyPKqt5utuxuBaRSTcZk13BFU82z4B3G2OXrGZ6F7PJa1AayKQjZd0XGVD15Cdi33Xo1chshRzJ5oYRbwMccaZclCgBYqte9PAtdJ9fBPAuW3obPpOVsH0fr9t6EXh45kvDiUD15CYOXs5wMvTgJ+mfrGVZnuXUbU3/cgS37Os4OXm227PMgqFE9wljVnB1ROpbPmFynczQPy40BLE5uwmYy00pfZdGWueT5Zh7Kyp47VhEqdezcfRMLVzi1MND3ZgvT41Wr9BQp4uzoM8CoqWfkuWRsjQGgXVxtGSWL+yRZhKjiPvdybg9wzWg+nwh9xOLfpe2ieTF5LnwEID023w7uk6p83ATNKchzUdfxQvl7HS8UcHOXar8aPefcBy2x+LKB6fiV0kboPjL4Wj1eHYOT62HkHavZomR1Jso3BDAL/o5XN2QC6Ohyd8OziNaQiIiIQuiJY8haAJOKAHItB6xSS5OFZJeoBXgSDLacAHIMS12+Fzwu28ITAfUhzx3eSjJDrn5D913Aklgoo7fUIzEsbH2ii6D++OagMvMJluqbszEkR+uOAhIw1pewzuKTjBQg8R54Tsw817MMzP17bYRwAUspciRsnGM2aColVclblaElY18U7nIyhp27/4Ua6ViujVex49W9zpFSJNzv/uQGzo4+g8defgFAGkBHW4RK212dRx56+hgSkUL7pDwodGOjFrleQx+7RCTn2Ard4POAlZfM597vU676jpnssp+nrNXHOSDVa7G/hS/xEKdDkOvZz2Rlz51McmYAjg5p5Ln37BhZWS0u58JM3TUxmwnIJGN5462zeOzlFxwd2ts3T66vSFnrIZ6FRPvC6NMPPJ6F2ObvJZ7Fddp9pYzZdbcBEMe3dJ6av30cQPd4FtcPbcTO3WZ3XpxMXffLrTSVI/dFFNEiXel5kYVj6/n+1HT6/TbaxwftuBwqe9K3UTKdCobP3MI8MUxrFz9wTKe7JmYtF0MkPJZ0plBHZYYlEgORPLee77cRrton2sCk+X3qcB2Dx1eAIVirx3JjwJpHV/bcweKt/qReo7f4649/hbHLqen0038XXwKiZBFA6KXtFudTl8sja2m/lLzy+mEPRfHu1lbouKB3aSmrlcPcHx27IuS34gt3z9dI+3mLhf7suw++REuheeBjIy8KfL2z+NA1gEvKkt8A1wTsS5Tk+Gp4zOs6SZRApDCmvYv3MW8cTMri8jwmUXoeuHoQ7+z/DT7ufBQVnBEREfcPPXEM+azo5r7t+90nCRQxkeq6fG7SAIJBc9nkxtf66mUP0bKqA56dUx8NuO9Sf8gxTDgkTp/INMvZw6U/bWrD/u/pVxvuWVz6ys502hvWx+1wvmtkw9L1JUpFrXdhb1pm6C7VSxhupeK+ZEITL18bmBhZZz3La0mkL/ltqV5C9WTWkY2dAmW8lWQMy/v32j5UacycWrKSHKccAppHCjtw9aAzR7osl/nD136PL292Tal56IljSF6SIZ9SS7MXffZuH6ORy/rSuvk4GbptwA1zphcAKZsG7M2yErmPALz8Aq130d///UfbcsPIa4TmVL/A08eyAXt98xiaL9+9CC1iRpu/wSnrq0OzOyUatk5qJL9l3PJPtDNMTSANiqP1SBwBm+dO4kHwGHjeAKN70UfGcsvEGmUWpW/MgD/oksyVXG8X6BOpjsWONdG7TR/7Hz6dK6desQlbVSuEP9wygXffX/78BL8plUofAlgCML/afXnIqCGOeT2gl8c83Ol0vlKkYE8sFgBQKpXe7XQ631rtfjxMxDGvD6yVMUcFZ0RERCHExSIiIqIQemmx+PVqd2AVEMe8PrAmxtwzOouIiIjeRi9JFhERET2MuFhEREQUQlwsIiIiCiEuFhEREYUQF4uIiIhC+D/6uMRPO7llogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde3c0bb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 4.349034786224365 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 4.0} is: \n",
      "[[10.  5. 10. ...  0.  0.  5.]\n",
      " [ 5. 15.  5. ...  0.  0. 10.]\n",
      " [10.  5. 10. ...  0.  0.  5.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 50. 35.  0.]\n",
      " [ 0.  0.  0. ... 35. 50.  5.]\n",
      " [ 5. 10.  5. ...  0.  5. 50.]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf89436a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 5.181172132492065 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 5.0} is: \n",
      "[[12.  6. 12. ...  0.  0.  6.]\n",
      " [ 6. 18.  6. ...  0.  0. 12.]\n",
      " [12.  6. 12. ...  0.  0.  6.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 60. 42.  0.]\n",
      " [ 0.  0.  0. ... 42. 60.  6.]\n",
      " [ 6. 12.  6. ...  0.  6. 60.]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf89a6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 6.3245463371276855 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 6.0} is: \n",
      "[[14.  7. 14. ...  0.  0.  7.]\n",
      " [ 7. 21.  7. ...  0.  0. 14.]\n",
      " [14.  7. 14. ...  0.  0.  7.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 70. 49.  0.]\n",
      " [ 0.  0.  0. ... 49. 70.  7.]\n",
      " [ 7. 14.  7. ...  0.  7. 70.]]\n"
     ]
    },
    {
     "data": {
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Ahgwg9k3Lktux1h7Orw1LYK1QSe4qYYngO7PWJ0t2zrStYZW6akmzlhLbryvSRV2T/pAitk1k12LC40hY7e4qxk/PhWdVXODHNX56BGf2nAt1kyaWzmJjbqyGPozrqpSieo9/ezJiW67bvu6arwBc64LKswveLqoIqCV90ChifGaXUy/7pxsF76sugGV1k7Jk+8rxU95X/UgxzmoercmPrL8VAQjGg3NQpPQHlvQdaF/Zlm5C1GH0TFneJ1kEpupn+H5aRpTy+mFJ76tOKzo4B1hDVD/Sku/H69dlTwWwXWJaQLuD6kKnjL/ZmXJNe5udqcAxFDiLehm9b3EWMwtb28JZkCuw173gNN4pqmNQ5Wh4+X1wFl4sDA8LoLE3lBOz4QpZbw+o1e2ZG8PvbhWjoofKxm/xED04OAvE79ealXVhhD7IYtVNJNVfnRv7bbXkmq1fsRJ8PsTkMDqvGgfRR5+jc5tSK0acjtGzcL5CgBxu5F7IPUf349FVr+D0cA66WDqPfLe2SEibnSl0HvluEFk6j3y3auOWgxX7f8tBtLqrESaD9SnoSxe+npZLpQhgxRgSFX8AXFgux7R5y0H0gFq/iCMJ5RJtTJf/azAfLuCejJe/lwyGpYLNx/UGBaxeR/wedNHZAEOt/Z1YAWesM5aj1GthnhPAI0vP3j6H+XvjMipuRJsbKlHCclVL97wt9NcuMWs1UssH7y8dGquU3RisnLWiGBWXei225sgmXH43usFyTOx7a38HZzeeTLZvqVFwNtRQQ0PRjuAsUrQ+P4p2t/5bT3tgqjSdxmaiqowqPqu6KIbQ9Dl99FTNdNru1nUOK4dn0Xmkkitj02mloCQoDIfmwmkSycb7OxVbbmRLEk9uazptd6V+ILRRw2+Q5ZbTuTidygaOHItOv/X50Vq9NJ2mUJT2ZCxO8Wq8vcVuzcuWNLOwFRTF8/c+HnnXqtjAeV8+RKVyyYL3QabadrTfrJf6Bb6z5UMFBxI4DKMbiPvbjeqrTKrdMA/6Pba7q5jB1LaQp8pRUVmt80sztfcOUqZTy2W1Nq5AUFbryLGKPSoHP21kdf5m3EygFAOOZ9iYQ83qQNZ3+nih7PXEl+njs1g5kGNiqVjowZZfWjm0LACsHMiDl2vxfBYwFpZNZJxQ6ydCb0yLPdjsTFVyczkPAXxUluO4+fGRFLzEj399fraGyZhZ2ApWl2l5ByqSaL1svw44q3ty9m45GC3MlcOzmCjFNPs8f9s56pVilpbjuBmWQK0hw8SkJFuuOgB+C/QQBRDVr0pAjtPOuRLHrRuLUqycj71KlTRgb+jv/k4QabV924Ze3+xMhf9JLadfV5zOoh9Zl2D74RWKzwJAxV1/YikGPpE7efb2OWzMjUUTNn30FCaWpkoOIY4RoCZN/iZXUXErp4ASSm4/JM/cSx2HurMzuLB1VFNXfM90qspUNfNq/hWaSVUP8eztcyHWgbVQtPZ3XCVtS7gQLghaUABj4jVoUXJI0Ydb1md1HCHdgi4aRz9SULzRsI5hSK0hihxWi4yiXlXpDtR1FrqwWS4Clkm7w/Y18g0p9U6RLsR5TxHy03CE1trUW+xuy0V9x28W1lxkFXkeFkKVVvZE23UyR+vIsSjS9Pr8KKaPF1wDxRPV0vNFt7sIog25FUB8DzpTUT8oQqyXp4me9EDFCVnLCutUZa4u4IAGLJ9hG7Z+oDqRWW/rSDeIS3oyrxyerfKqSL0BfdmZqrTvjva/XdYRxZcgB+MsdirgNGGOcjCqdF4ruaAUrmQYBKeXL4McgxUNeF2tJB7+QsmaX+1C9XAwnhjn5SWJ6vZwIaYOvrdpxL4gk6bsdhGcVyQoy2OHAUQmTmVP+923ACUFdnlmR4oakUNPucnYdrXvHI9eV/MlE8hEi9exsngmUp0fe53jJbEdbkCD6uUJ6QG0bKYxPmsBc/ZvW97rf7+ynoWM5G0cnp5AgW12MaoJ1xsH+6vkZSQbZryev43tq+UctS9ePhO2befRbnit/R08/sKDTa7Thhpq6OLSjhBDsrFR9N5+sLZ7WzY1/HZs78oBtI50I8Qnibvt5INPBDEEqMBam52pgJ/41icmMfZ0Fikz291V7OvG2IqVw7OBC5k+egrf+kTBQO69/5oifeLJHBOCvbBQc/adp78q9KxuBqg03aq0ZBv96rdcDOeUxPosPJz6Hsut8ITSebQ6F94nN+ZhZZTt1vFabAv1LNonezq39nfc09oiIQFESlTqunadzN1UllS+9245WEtTGFt84vSFijJmPwZlUNO+Lt3zNgCIvKrVQkjRSrmg1v4ibWG7W7cm8hkNT3juO1cggtOzhgCivDGsfNhEgKDVV2AMy3j31R2b5lG1IADA2NMZtm56CXg4DQwDKhMtF/fY02NlmZMhI5oHmNIXyUVfQNHjftiXrYpKzcqWagMo3fhRbUJceLpYWbcqhFP1qWJxTZ7fmMsw+aDog1DkgrWBikjaFt893wWtUhNLcd+U+sHTbTsrh2cjPYCWTfWv9j31mRtrlbG6o1D/ANOpbp5WEV/UER8oVMTqt8mynqXIXtuONWTH6CwuZlg969DV774nh2vZb31iEnvvvyZqz5PvPQcwfqRWf2Kh4YMUZz05ZWg/18WjbXiKNDt266vCvlgshZ0nz/RJUr2GF3JOEae2PzpvVjGY4jZSugWtW5/36rW6JFUm27nQOWEfvI2JY/CianlWGzsGRb16VinPWdErY50CSRZ2vx2dxY7gLLKxUlNvXlDEtsmHqKyvsun8WNShy344ZJXtS+cJ1TKb0t77r8Fzd70MADi33I4Un9oH4ia8E17bWjmQB/Mt4d40zaX8MNbee3MUCdwLAR8iKZX1AxX7OqhfQHGSeRB57zkAEVsMoIbZAGKsiIK9irD2cd32FPfuKXmL2TMZ8t161jTOuVXoaj5ZoO4BrG1ZJaPFaGhb3kFX809x+kFi+/bbpoiu11QssW0pbceRrFFwNtRQQ0PRjuAs8q16jEQANfivgmaicua3NWHqKaCmQ71HHYIFUAEFRwEAIzObAGKFJGVTL4Bvb7EbYRxSFNzpUe3+HmekbSvop18bhexfjdeOn7qBSQhQSepV8k74oFsQfIQ1VXMuLUdUjK1epwWL9Ss7KE4Gr/WTzKtn4vo5NkZMS/UBkFNbuNt0O8ORFVXaZR8VMwPEpvF2NwbDWQ7sQsLq7RidRQpnoXBv/rb6CCrE+DFabITFGQCxzKw4iUFwb3qcqq4iBfdO6Vu8YDiekpI4CL3XDw9h6weqzUzh3uy3nUeryPXqBqqPWIPfAHWsAQFdns1fcQPeHHlu4h5+pJbMxyGrS9G+1UQICcCjZW0AnxTOIoVBse8mpVOwc6j9DyLVAF2X58HMOi8EZ7EjOIt+plN1zebvuo9HlXWMz8fyd51beOZDewAAnUfOyrNVPSnF2us/e9rkJamemT/SDfqU5YSLOrkQGzuDruCRwmp/p5aRjH+nXNSVy6GZuXXkWBQPsrfYLUyV8kGrmdOr17NE1HKYcOGV1zRLl62LY6i5XLPPYuLc7EzVNqveLQdDBrVBZJ3b2DdF+bJfes62zDi0v+rmnYqh0a8fKeqXpyW0T2WoU45w75SuyY7x7N+4tm9/lHY8Z0FKIeqUvBSEqft212fgXpI1iQIVjqDdXQ2bjWIs+lkI2B/PSsKF7yUx8hRiXlg9PXnsJpfyDVFxyloY+s2/7Qfn0PbXWloi5aJjcfAsG57VQ+tPKQyVPM7CziHLKXai3/eW4mZsHXrdjssj6xOjbXnzZa9by4p3T/u1nbB6jYKzoYYaGop2BGeRykimcjV1D4onABD8OTQQr57e1t/DsmfkKGy9QB1Yo3Wyr898aE/kpq4ntQ0PP4gzAnz7u+I/+qUCGIQ3ABDpVewp1C8VQD+MgNLSPW+rxbO4kFQAioFQfZI3Zynql0qQ/QPKVADSDy3Ld1BL7yDj1usaVYtkRamUCKP9BipukHoxbcMTa9h2LZ5FidfR9/ON7zyEv/rLF64cnUW/eBbKtgWvSEGpAUVMCiCPNhkGyanu+5sQA9dAcApA2pFsY66AgAcbfPBWPVtTGqZiH7AuC5TSGBMrh2cj7EIYayKeRaoNjjf0qcxtwlgOim/w4ll49YWAPJ2pmtv4zMJWvCmiDl0neWOwqEwdd8o6E4X2S+kPzMLU/th4FtoXHUerz5zbOBFerIkIBNdHjOE4UvEsWEZjaJB6i1XMCq8PV3w8i5SCc005C7oqO5wFLSHWBMZFEUxgR+sRlskRUH7t4GB4lnoLQq8nls6GyVZ9Q+eRs3jurpcx9nSx6bDs8qExbN30EvaWdWrAV4vbL5R+1W/ddBRWrUFzeMqxDRtQ1rMCdW+7NsTkaJUKVBLnS+HnN35Y/A7k47XPkookNsVAuKnw5NZ55MIJXF+34io0dgakDm7knUdK/YNnYZFn9Ho/hGxI93cgD/3wyOuvjhtAUCSfuO3aEJnN6q7YrqfTsJ6xQPHdLJdJhrpSLxMGWd8Qtq3vgve7t12L14uy+4qM7n0hkbLI/qciZSn60cZAmFmYCi+C8Rf0Wa0PqF4AXyTbpi/JvofXQtkZTGEZ12F9nqJetVEwOhdQbFi05dt4FlZs0lOap1zVRsxJrBzIg4hEVOf08bwy98opqQrXGaknMheaRVRk3UJURypSlkW9Bg5RuL7WYrcWfUu5E2A29Gt6f6dwnIu75FK7W4+UxXcec6lF/XYhc5zaX0s2RaYqy/V6yDeT6PegSFnafipS1vTxLHz3Fllq+3XVcBY9VGYrQlppLlO53IoLvcWuk2ekWmgax2F9fjTSdfDDKbxO696S+7qxyTEsssOz2Pfwmu91KpxA0dYUJpYq2X0GU7WNTi0bvcVu8BRMep2WHAHrL+pC+N+KPLxHmlg6G1lllGyIf3Iw8/c+HkHvQ3ljntQ+6ztTgBPbmD56KnCROh/qdcp3xJSNXNgeVwHUId4AIr0N9Q0ejJ7t0OvUG2vkdUrcx2HJ02vmDEBwAKvBvaWvntepkhfQh8Q5JrfqAdM2O9vzOm2sIQ011NBQtCM4i34UZDn5bVla6/xky3jhzJYMotHS3vuvQbt7siYGADH7bdugh+pzd72M3Y/GWb94mlkcRHFS+kjG4rSvQtoxCCvr17Zt/UochyIerdWBMvH0cYSyK4dng/JX6wo4AA33d8TBFxw5hhnUT+Tpo6eiZFArxhtSOUqKNxaUFeJ6Cifa7q7W0JMnbrsWN374yVrfGHrO4nIo+lgcS6EviHVJKVyHdQUnUlj1BfYZANF8qiVjfX42tB/aFMud9kPD6amObHKxG73/NoCzk0MZQoo+DV3yMtCwqQCWD41h1/xslHdTUwFomHayqvxAKLu2jhyLgsqqTEviRzqzsBUmfLkMtsvybGv3ozlevPUMNp6+LhJzFGyjC9DGimB5CyBjfs/d8zfjxVvPAEBow9bvkeoErBJQY5EW5Lv6c471fy4wteJQ8cZgLHaxTx/PaiJirf5S8auKZkbaJtl3pAsdKCxeniu4hlks/p8NGd88L9Dp47ORDo397C12w5xNPth13RKKtgZHx/VMutNHTwX9k76/lQM5Jh+s16GQAUUn857qhFovDexSVXb4ojufBuEYLJoSAFDmoginRHmf1hXFXUzos2ZBbt30EpZxXeSfsvH0ddi66SVsLgiCs9S9pOJtpkj70bvlYKifbW8uTEX1A741pFCs1v0t2t3KzZ7K0JUD58LHD1TWEG+e+f/GXBY+xjCnh2dr80gYPq0L00djtKa1hqA8mYuxHATEb4bkmVw5dqsb8GJkFmke6hgPjlMxOyzHsuOnR6Jnirqq/tMJLIKWO1yJXtPDZPz0CCaWtsoNoBO1qe1WbRe5f5WL6MkYSSNnh8dZ7QhQ1nbh3kp6mtr4A3ofqDs2ATEUWbNyKyCmH1BLnXosXJvtMB7G2NPVZkJzLoBog1FFmcK1BzmoFabb6yIlHdtQJSv7RIuMhbjbjZZlbYRxK56FCE/GX8T6tViyimK+L431oeVsPZ6zFpAwQRqnsVTeEasE13nz4Pz6bXobfwqYNggOnoK32w3HOwQ9V4WaLw+wLVBWo+BsqKGGhqJw5DF0AAAgAElEQVQdLYYM4wjDTNoWbBWZ00RH4HlPcsden58N7S0FkA1qyj1ATjSRrVU/Qh1Cu7uKsacLUWfrppcws1CZVEkVEKou01ozp3dSsw2tH4g5Fo8KNrbqR0p8CzgJMZ3yVFaua2MuC4F32a8Qq6O7GtVtociKxejdclA4ryrSl4cABcy7hq9vYAxO5UoonnjpEOzzlSgRE/ELts7otEf927PfMv9njhnbVzt/g8i+c+8b2OxMYeSFVwhnkWVZF8BfAXgZQC/P8+/Lsmw3gN8E0AHQBfCjeZ7/xfnUn8Loe+KIsmcp2ZX3Us8q+8gPeUVkZaDyL/FEAX0OQHD75qKdWbimFBdikYPPRR+5aNQtJoELjWIB29D6gXob2k+rcFOXbZXr+Yz92DfmsggePX30VKHYFGTnZmcqigNhx6v9ISn2xF73xI/U9xCBkbqroR+K3yD2wFqgqJNi2Xa3Lia0u6uhvmn9TvUblD5636MnxqgY7MUy1Tlgm3YOFZ9js9x7eqZh6WJwFu/M83xFfn8EwFfzPP/nWZZ9pPx9z/lUbLXXKddflav1Wb1Psh+dyq7eRqIKP6Aep7IOr44/Jt23lw+NYezpgsNYPzkeRbC2G5yVT7UN6xPANrR+AFg/OR61DcTBb5TUaqH1cl4DyrUc266TubsRWcuN+vfo+0kpde14Ky4sMc+1GmJif6wFinWof0xqHCmOi/VF36kzFs20pmhYj7NQK5Buut44+zmi8ZlIv+aB1V5wH3fpUugs3g3g18u/fx3Aj1xIZXqK87c9SaipXj40huVDY2Gh6v12ia8vUG2rAU6rCiH78fP3zMJW+AcUHMNKqeHn81QYWiK0mv4rMwtb2P3oOF689Uyokxr2lcOzePb2uQAJ9haZt8A0UhjrZxsbc1mon7R8aKzW12dvn+sbOn5mYSuCIE8fPVU61Z2qecFqOZ0jJY8tnnzwiQJiLrDsmYUtTB895Yog6h0LiHJwf2Wp4GJaOTwbnLNaR46Fjbq1v1Nj8QNqthybbYcUOXsJ8dtjvRY6vnxozD30qKQPfS2veWOPcEXld+zNcz/xZbucxQVZQ7Is+3MAf4FC8P2VPM8/n2XZWp7nk+X9DMBf8HeKLtQakmJR9b7929r3KdemNNmk4B2aOBUtrJrAKgCRJWRmYStYSV73sbUa0ExP9MKh68miP6XJz0YLUzg0F4INeAPE7vJeSgTl0FpHjkVQaLvAPeuP6gD0Xdj3Z61PS/e8zQ2LSNd0FdM8YJ62b7+TVOoHz4UbqMLle1ydhhEk1+pZTmw/NOCQnRfWZclyx9aioW3b+pbueVsE3af7vVJrfwdfW/siNlaef0Vc1N+e5/lSlmV/E8BXsix7Rm/meZ5nWebuRlmW3Q3gbgBo47oL7EZDDTV0qemi4SyyLPs4gHUA7wfwjjzPT2VZNgvg9/M8/+/6PZviLIAY0eb91ms2F4h9xiap4T2y0F7wGpvhnM9q0p9U+Dptk3WSCuhulSpx96PjUdBhknciWhnaCwDMNoAiKvnuR8dDGQYLsrk8LLeUQoQqJ2ZRkKnkTilFpsUMKOfE+lwX7xKLYbkxm9gHiDkAkk22o+OyCl4Np5gCVaXmiH2x36ANNDwMzkLvax8szsLDmwTRzIDRthOw97w3iyzLdgEYyfP8r8q/vwLgfwfw/QBWRcG5O8/zn+1XV7/NYhjyXlS/wKeeyOKxnBo5iWU8kccTb1RcseAqZflJL956BueW2xFIilHHAdQ041HezbINWz9QKQhVtNCI4doHD3AWIQPNxqeLmn21gYi9WJdADJTiuDQIkBcVK3VA6IbhZSfXtuzf1pxp5xgwQWucqN62Xs9qVzO5O21qOZvh3rue+q1j1u/bixb2SkXKmgHw24VaAi0AX8jz/D9lWbYA4LeyLLsTwLMAfvQC2hiKsyBFL8TRFKdemF00pCL3aGzH11O1qKuec8RS9TGPBnOWfuTLh8ZwbrmNkZlNbMxVItl4ueHTxNePaApVcxmzlzF3CFDY7xX7QKvC8qExNzJXivgBcwwbc2PBZTpsTGJhGYbU1JtyF1dKzbdH7JviYwbNaeogsWWGRyqkv0HtJ4Ck67nXr2jD20ZftkvnvVnkeb4I4Cbn+ioK7uKikJqa+DvSBMsurslX1NFJAS/qJxCoMxVxAlW78QZC35HeYjdgDHqL3bDI6smL6hwEaWMuC2CrXSdz7DqZYWPuOsy/6zkAwNoDN2DlQGGeXD40h30PV8+29ncCCIyUaqMQi0ajZwEE5zv2d/5dzwEPt+KP2IhourlNyHVS8K3g7xKjMn20zt2FqGfldcbSePHWM9h7/zVhPBbXwGuMHmU5qcCSG+7vubsmMbF00I3pYM2rQMkxlNc1rQMMt8AUjWEBl745nqlTDxnAj961MZfhzJ5zmD5a4TxaRwQvYax8PcRijppwPfHPGg1GRoffXhq4d0MNNTQU7WhHMmB7Ck5L/cxSSnVgVaUHsKYqoO6t6eUN6cduejoLAJGCc/KO57H2wA3hnsb8tKepJ1YpWR2AmnW1LzZCudW3KNmIWlYnon3zQE12fmz8Tw0hQJ2LVRgqtgOIzbte37wsbEraV40mTkWwhgtQMJ+n37DfiCfKpEy9rN9z3LN1WUVvP9HJU5pecdG9+1FNMeboKjwlUZRQxrkffXiiJaZ8nZJTvWCyFENSH4j1WrXyPgFd46ezCiz1wA2YvON59D45E+kTNMCN9lG17PrBcLHZsPR8hgvizJ5zIbCLDRy8cng2svpQPNLF/eKtL2P3o+MhNib72s/93m4gqq+o0jfU+x1Ey85UbdOyc8trFHM0HoXqoLRsJWZW9cYBoSsxk/EhUoCo1Fg3S3EWiMUI3YRWRFGsYk0Uwg+xrkfD6FmyAsd2xZAdzVmcLyjLuiJbUFbqJBgmrsQgjoHX+XGqnGpPc+t0BtRPxNZHl7H02N7Il8KevmrGTAHUrPu+fpS8z49OTylmXhs/PRIpHVPu7J4VyrpL6zjZPlNC2n4+e/tcIcNLbhfdBDlPnE/Pec4CzQCEHCAWKMUNVLmIKFWDmS+bbS1lZrWgLH03KfJAWanwC3ZtaJ4Rklr2SK8kKGtHEC0ACve19wEEpZf6bKj1wFV+AhGC0370KfFoZqFaSOoDohsHrS2kdncVy4cqtOfyoTHgsb1B+Ujy0jNSYWrNm54IED1TjoXPW4ex8dMj2LrpJew6Ph4tSpSLatd83B5NoHrN24AtJmRkZjMoRDuPfDfMcbH4RzB9NOYwyFGoqKS5Ze2mG35L9CkqeHW8zE+yPj8aInMBdbRqaLdMmtwO5apntF4PAZsSpcMmI+UYPCe6TwWnBDxim+SE9P1Y6i12MTJxBQa/OfyaO10dgz3Jh5V/U+Age98DBWlZTUBMsqcp4IOS+vUFiE+51AlB6t1XfA69T87UThhvPJZ0PKlgO3aeFA7tcREp+7+HCSEQDEDgFFSMsZnPPCxLCkCmICrrFJjifDjnKu+T0/Ay0bFvCt1XzkLb0Lo4tx7uoh8GQknxIDYhNseveiJmLbOcObkpfX47oKzGGtJQQw0NRTtCDMnGRoudtvztnb6bemoZ7TBZRJ4wk4vdKG+IxV9MPvhEqE812hV+odrBVbGnsij7tV6e6uvzxf+aqT0VSyKcfsrFODJnZPX4ZDkfH13GxCdnXCBZPy5H51TZ4Qj9+NFl9D45E8YAmOCvx2N0KgFpQAwistxRkQXtu+LF+12/7yVuQVl+tlsogBGNK5yoDjelQXj53ugyD6B4z/s7Ico4gFA/n6f4yKRSVhXI+tTZMIg1h+aishZybkWMmvLR5CjxHOdorUvlM4mcDYEicviht4Uym53t5Q3ZMWJISsE5bPAbYDA6bljlpJKy7YDvq5H67ZkRPTZyGEWV+qxwUXvQXk9s0rZUn6CkCkv1vlTy/DCsGKLPt8xCUvOrVf6yD5wPbccjLzanbtS2n6zXiqK2LfXvUbLzp0pQfX/0OdGFmvLz8Hw1VKTygt/YesNz8p2lTLp8Fzp3r4hvyMWkVBZ1wF+M1j3bW+iedUDL6ofl6UM8K0s/Wd6WL/J6jEcLzOsj4OsLtJ+2j1o/4C8cS1Y3A8SbMU8+ZpVnv1JOatYHQz9ozw/EvgegsoBwLnRRe4GQrYzu4Re87+GZD+1J5w3pM/fe3LC/YWxmY01Z00LekMTzKTyR97y2qS7zJFUyq3memCFt48jZL2LruavAGtLkDWnyhjR5Q3ZO3pBGwdlQQw0NRTuas9gueUAse7+mt2iSDIW/myRDTZKhfrQjdBZNkqEmyVCTZKjql+2rHZ/dcLxD8FIkGdrRnIXdIDwFWpM3BKGNJm8Iot9arskbcpnzhlxq2o7pVHfcFDvKe6lnm7whxaJRVp3P2I+9yRvS5A3ZUWS11x5noR+ix1nYCbYfnbKj3kaiMjzQ5A3h2Jq8IQj1Rd+pM5Ymb0hDDTX0qqIdv1koy8/flu2kprpJMtQkGQLEgU2sFU2SoTpdVWIIgNrLaB05FrF6nNR2dzWwycTM632gYsEVOARUyrWUKKKJWvY9fLJmNg31HB+sVGZdY09fB2ArXCe7rVBjL8bksPWzja2bXgKW2zUIM/vKTaPziK8EjeReI7drMBmFSVPX0c8c7CorqafZHwfQsXoJsv4zC1M1MTAVHJiBYvRbSC0W3WBYtp85PlK4D1iYHpTc9hOo+4ooWatf75aDQb/Ca57y90KD+e78zSIBg/Vktch/wpHn9L7VaVBhp2U0xkNBo8GstlGe/tOINxO1XJA8q8TKgWvDCx2ZmcSu49RhjNae83QXXhs1R6MD1wJl1PD1+QoaXsTRKPKGUB/Tb6F5RAuDzg3r8Kw1w5DCuIeJ7m31VP2IVgu1Ogwar+o3Uu2kdAYpshGtvH4Cdf2C1y8SMSuXmnb8ZmEnz+7wnqbdu08Kykk5JW2CIdZBuLeKA8uHxrCv2wknhN7bmBurLRTvI1s+NIbx05Xj0+5HudCr9snFeBGu7OZh2yDbPH6a3q/jARo+/bE1dB6ZKhf2KvYV04qVw7OuZWLFpG5UIvinKOsrJ71APTo/KgaGiOGYCpzhyntvBkquT+eW5lXdnCPvYYP1AHx/C+V+KDJww6qie89VSmjHCtMTJalnaVAHMIo/0+ivoPT8drSPQGEF47oIeB8gTjVRPrc2Pxpg8lrvudHht4Adr7NoqKGGdgbtCARnyuvUuhdbiDRQnfYaPYkngI0YrfI/21KvTgXoeKCjlFs0lZQWDel5GqbQn4MiZWmMCY5VA+iyjZS4Ek7E+3pYemxvqEejftu4jfPveg69T864GAeNS5kaL9+D9p3tBjfrjy4X/7+/OuGUU9QoVjQJ773/mqGRmtoPLW+jTwGFyVnrtqd575aDAY3LcuQwrBfztz4xib33XxP1J6X38LBEOh7OIZNps15GxNKyQOGR/LqPrbmJnxe/8MbwfLu7euUhOPOts5HikqzVJFAhJI8ci36ra+6+bnHNuuWyjHefL2Tfw8VHUbyM+CO1sSE0yI7Nddp5JA5221vsYnKxi4mlg+4CXj40hvkjxd/d265FBwdr1higEj8sJJz1F9fSbQBFGLvOI6U37WNjmH/Xc1hcngZwXViAKydnw4dO0eRZ7MUM4vEXVG3mAWnpxM9kHEsvEBBQLoJy49qHCqSlliWKGTMLW4IbORvKKv5gLaFb4OamIofqaAL25OR4qNuOt40C+7D7UR4ecTsWZFaED6jrFgbpcSiOqaVO69/96CzW56trVNoq2Kxoe82N6G77NXL2Kkoy5JlOlRRZRxOllRdJ/LDti1qfHw0gowJeXUymVbLpSVpkEYsVlza3htbPf/x9Zs+5IFuPnx4J5l+Ogf2w40+RrZ9t7DqZY/z0SHR/cXka+2dWcGbPOZxbbuPccrtv3xX0poA0mmvJKYzMbIb5bXdXw1hS9SrpBjOxdBbjp0dq/fb6pCbSfspO24+NuSw5r6pv0s15GEoF64k30LSr+sZc1ve+9ovEsXtuCfx+tmNRS9GO4CxS1NrfCUqmnvkdwXnLWBQ2kjFZP7L4u05W2vvAth05hmlUJ5qKBlwYNUey8h5QKrpKVlQhymQn291VdG+rwulTiamxHNQ0xzFou170JmVX2YbWzzZYf9ggD88CuA4n9lyPG9/wAk588/pybvIwnyxr86QqsU8VBmEU55ZztLunI9OqcmreBsF2iajkvDLyeZHW0VqlJBpUrWd10nbUdGotQGr90JCBbIewdyUrNm7MZZhGXRnM8Q3yLvasWjoOnUf23XIWLKNjABDloSVtR8G5ozeLWsQlZ3fnQgXEDVtwFqn74aWV92xcTF5TkxQ/BF3IhDN7CVyA4gVXuoyYU9HcGIQ0s4/klPY9fLJm0rVzUbVR1z+tHMixMTcWRIuNuSxgL058s9gwAKD3yAyAmOuyoeO8AD1VOoMRjJ/OavoGG2CGxI2YqQ/U8W7l8CxGZs6U5a4JoojWy7nTxdpPDPFMi3bhkqvhdWu5aHVXa6KuJYXTDwLTeaQ+K944NDEUsUL8VornY11fFAXd6ddVJYY01FBDO4N2hDWkX65Tm4siFRRW41lorEFl1xlTwKJCrQUEqMQSG+OQlgtP856KDapiDPumylMFddks6tPHs1o8C5Iqy+zYgXqwX6CyWlDfoFYJtUhoGzZbOn/r9eVDY2UKxNORfO7FneRc6alntf2Wk/Icu1J5Xz28iFonLHnKRhXHbBxULQPU42R4ddpvwX6H+s44j947tWTBirY9a32zIR+eGv361ROw10awtuni1IxnJ9++IN1E7AZhIcWD0g960ZxTqQBs1Grr5ep9rN5CABAie7Mekg10ou1q23bhk9rdVfTu64XcqiRuKGoapkys5lt1cdfx9Ba7OPGZt0apAPi8mrU9qxOA0G6RCqBghLlx6nvUZz2Tt2cRIdkUjbqhAkXaP7ZjY4Xw27GpADQqvBeIWAFdHjjLG1fw/pVDxksFQDcBe3jRiqdz/YdPfW5o0+mO3yw8m/crGd3btg0MF92bH7nKh14fgf7Rvb18Ee1ukYNUF69Xv1JqTu2JzdyqnBt+pJ7Zz5sv7114pyoQb3zaN1uHDQsAVO/AbgDe4lv8whux/8f/uDYnduHogiJ+AagW9rc+MYnXfWwttBPaLDN9AbHuReecOBE+rxYmO19eWD99nvdtP7W/Ni0CNxBb/5FzX8DW4tKVg7NIkdVW8/crEd1b21ZAz6Do3uFUPy7KRQOMsQrXfmP3Fsn8kW5IvAMgaiMVDIZUwNXjcem9kFsVwBL2RgrOYUjrt2PR5ECt/Z0CY3GoKJNymFMLQIju/eATmFmom9Pb3dWKo5Q6xspYpXYTsXFW1+cLB8OZhS2XrR97egwrhwush0Ywb3dXsXVXkWapd2+3inIum1hh5boOwFroKyOIeWS55627JjH29HVYOTwe8DXEyNhxrRyexa6TRXT59sNroX1yQBrde+TM8GrLRsHZUEMNDUU7mrPYLinb5u3YXmDTVHRv0rDRvc/sOVfTIQCzWDkQZ0rXNHgaFi/oW8p6rauxQqapJwEI967a8NLsaT3zH+3hWfhwb6A4VZfK+/Pveg69hRmntko0UFFq5fBslBme72FjLgteukW7xbMzC1sB7o2HWzXRA4gBbzxJJ5YqqLYqoG2KS5LHKfRuOVgzkW7d9BImlnzxs3XkGGZQwb21HFBxL71bDqLVXcWLt57BxFKlAJ0GojCErBeow73DNSk/9vR1QQHM6OxjT4+Fsqq3ePHWM3jdx9awMTcHcjLEihT9Kuepu4rWxqssunc/ra8nV9sgrSpTDxPqjc8p2f6xrn5KOG4AmvHaKkGf+VABttKYoKRazk+pn/fVEgTEwCML9rIWCKDQKxC4Fdp14nZ4MUFUUWnfkW1LFaiEjfP6jW94IfiokKzeSMlaoNiOWru0DqvToq+R7Xs/XyX73vSaKjyt0lkte0Bl7bPKUNsen08llbJ+PgBqmdUB4GtrX8TGylWQkWxYYoCWlFxtg6kC8a5O+VZ9RkheVC6vbosWRfmxKrqOC4kwbq8N1bYXH3xenKgPV320AKcQOFbqB/zYjVX/pwCM1syDdozPlMCtpcf2Bu0+F7Nq5j10Y2rOOA69d+Mbeug9MhM2DV4fPz2HE7ger+/GzngMvKORrLjA1ZIBVIpaDTLc7hYxMzwulIuMYDDWZ61IrI/6LqDQe7BOrZd9s4da4DIl9mbYGITLYN4RO68pSBX1MRbVrNRb7GJkYnhmYUdvFva09jTdmgogbACIIwRpuH5vMwiLvDNVO+Hty7CngcfeAn6mcvWCnVnYcgO8WO31xtwcdh0fx2anwAgUi8kP9W69bD0TqTWvKjSbuTztprtUKj1pJZk+XgUOZj2TDz6BtffejJYokanA8yw6NgrY8mNjmMFWwJVwUc+/6zmc+Ob1bh3cHHUeObcaZJnKRBtvxML5rWlSLRwowwOqCZp122/EIl6nj55yrR5wsECM+rZeKiSBSrSx3JlnPSR530etbGcKI9/4eu3ZFO3ozQIYDPnWsHoWqKX39VmXWyjhvJZV9thGW0dgB404oNcBYKJkMWl9mZCyHoyYWm390KaPz9ZePNvQ+nldc6bouCwMe2LpLKaP+85Vu07mYcMAgBPfvL7MkKV5UW8O86JmO12UUZ3zcbCdXSfzwP7Pv+u5oDfBY3uBPeeiOih2eTomWm/0RGU75CQ4N3Zj17nTDai32AUOz0bvn5Yvcm+sN9Y1xX3zRAubV5VtTCydrXEbrJd/b3amahG1dAPxuD37HprgNw011NBFpx3PWQxDg9yHvfueOBIFpwXgOR95sUCtqAP4odx4j5yM5oUgqUKKcG/FLnh6gZZpO5weJqRgdU/l9BjZaU8e4jmmjyMoOm98wwtYO34D1udHMV4ihTfm0sFoUzoLvd69bU/Apqx883pMy8m+66QTdkA4A3I3gdsxOovpo+qAVYpNSAP6WkeOYeOetwXuonfLwSiEIIn1xaJXxR1ovfpOWGaQzgLCIXp5Q/rpLLw595wdR0avkoxkl5KsaFL7X7JxWTdfqzdRUScSDRwLB30ybOxHLmA1rwUxSOJjqo7AikJaP1A3vwKIAEieElBBcCxLqDbh1mvHb8DkHc+j98kZvHhrYUrc/ei4C8jS8SuxraqNClK+MZcFczAtJFZW54YBAK8vgxuxzOSDTwQRL/aArTZjKg89wFlrfyeyJEw+eKzWBuds+dAYJkqdAu/3s85ZXVqkFIeKulO1DYb3rYu6jlOTXHkUzSMAvDC8zmJHm06B4RzJgHgSLVKQpLBhfd5TnnmLHajj8a3Si/Bb71lVfOmiZp9ptgPiuAYK1fXIyrPqmMZNoAr5/13XbJpKO5AysU4snU36qZBevPUMxp6+zjXZ2bmhvoKWFpW9OefWYY8KWcV6eKZq7bP+1meA6hCwPi6E1nt6E513fYZlWK8X4lHL6dgA9G0rUnxq5vmStD3WwbANtuyTE09cWb4h/TaL8yGNaZm6bwEx6qUKxHZ0DUJjP3y7IQ3iLEg2R4n3MWuEb1VI0mnO4yyUNNmwfjTcqDbmsgCi4iaiHI9+uLrhjJ8eCfEqCaqipWTfwyeDYxaxEh7ZueVC4obBqFtFGLhY/NJ3p0A7nRsvA7q+A8/JzpZncqYbP/xkhH/wxqF1DcNZ2G8o/C3fpkd6oDCnytp7bw5cYiqaOtux8/L4Cw8O7XXaKDgbaqihoeiq0lmE3fs87nsAJqDYjek0pPqEwEEklKvqIl+HgddFGzqdkWYWtgp9QAnGIqc0aeoHELVhTzkq4SjfUgdSnNSj4HlBEUojhWlfC1h1YR7dmCtO/PV5YOOxChq+9sANtZNXRTMlRhjz5qNwSS/ehsc6U4YntF31EuQq9GRVLpHOaDxlCeWOqDTFM8hx7xYGU471C639nVCfcpTY33HDJQBIJnLmt7lWOsyl3P8JO2ebrcVu0MeE9mGg7+UYWovdqF89AOcmrpKwesBwOothg99o3Iue04bCvSkGTAu72O6uFi+hBHBZUoix1ZADVVg+spKUW9fnZ4MYoDlIdz9agLEmls7W5XBHQ671A5WFg3gAjmvlQI4Xb30Z55bzYM04s+dcbZGpiLAmyYao2FOdy9oDhdKz9f5WWGTr86OhXktLEnOBbbj1EqYu863vLkR9R7l5UAQ176172x5MH602wJUyEBKfU+qhzsbzm9LNtNVdrSmRV0q/najO8ntJ6TaAShRZKeH9k4vdMGaG9AMQtU9riIpdNfHLjs3cH7nSFJw7MW8IgKTsa30UbNQolqV+wIuNANT1JXpfNzo9mbTvVpdi6/fomQ/tCboE+/FadCIjNml/lHRz5by86Uvfxu889HfD8wwoo6TxMagXIHI05XPDnCQAojpV92MDIw0iO4+M6uXpedSHBaj0GECcEoF6hBOfeWstf4qn7LZ9YXnWCxQbFWNvKKfGeBb2m2LbNm8IULz/Gz/8ZDFf+zvbyhuyIzaLnajgBMQWLh+FNVl6cF173bOMaL39INHz9z5exCCQWB2ptmomQIclZz+s5cSFI0uf210/WZByQvQX+ZH3FKfVQ793GADcsHp2Y+DGQdMpSYPJkCza1VoYrLLXJmRK9cnWr0pDa5GiYpFYGC8AT2qsep1krT86PqsgtZaRJcGE9AvBp/0Diu+nUXA21FBDF52uCp0F4OAsHE5hIM6iMxXFmiBwhtcou0c4C1TKUU1BANRPmOg0F5mZCkTVu0wsnS1OMiL7FAlKPICZA8stoJSf1dxGHcbyoUJ8UpOszewdn9BTNYTmc3e9jI4EwaWOghzFe955FL/5p5UyzqNI5Dwepx/QPmjiZcbjXDlQ4Cx2idIydaoGMFwJ8vKC8JIDYFmOt+B4qrwqQKXYtu76VDp6nJNyZyncjGJwAqI5tHIAACAASURBVPcr9xWMxm9Dx8H8JoAv6nj+JMPSjt8shiFCrtcTH03QpB+ejX+Xf090HB3JLQeLzNMPPhFN7oRVGJVtsx4bWFjZRoox0+WmoAuiV7ZHNpkQZXosepB1y5pq/UCscFX7O+dJsR0rB3JMPljf3BQRaBd8EfClqJfRvcdPjwTsxm/+6UH8o+89hj/Ca2pseDH+2GVeRYc4e3ysZxo/PVf+XzDGHgTeXtu66SXMLFRRwz2PX+2jimXjp+cwsRTjF3rwwwV4dSl57SoVGJMzmFm4plLYm+91UFveu9J7wwO8Yxqos8iy7NcA/AMA38nz/O+U13YD+E0AHQBdAD+a5/lfZFmWAfjXAH4QwEsA7sjz/PigTuz06N5WKaoQ3e1G9075I1jSvqsSTwMBeyCvfhp3HZeldnc1KD8tAtTqVMIJZVIicMOyAYTe9KVv4yufenvQARAQppBqPWk9sNrNn34qcCw2yji5OcruFvCmZDnUZ2+fCxG4zi23a3Xzb46PCk6+g9aRY5GlSBWh3My0T1ZpaoP2WoAW54B1W2uRF12cyFYbYZx6IQXLbSe69zCcxQMAPgfgN+TaRwB8Nc/zf55l2UfK3/cAuBXAa8t/bwHwy+X/fSnfikWDyCnHQG83xbefFBRBcqJOHz0Vytj7VVg6AJgKYoRuIvx4dXFFi1BcvwN7fHg2+ujV94HPsaxuOF4Wdfad/eRHysXc7q4GsYFt2BMlpW23IfnZV8KsWe/G7XPRHNrNpyUimkdcHF/51NvxAz/zjWAlidjsQ5XSVMMNsO9s9yufejtwoA6P1xSGfE+e63qKNOHyyoEqHMAgi4pyLhb1mool4dVrcSbKxfKbmFAl9aG5WuAgpeJ95ugtdkPSb0AcHrEnev/byUg2cLPI8/xrWZZ1zOV3A3hH+fevA/h9FJvFuwH8Rl6wK09mWTaZZdlsnud9c7dlY0XnrbypC0qdbqytmf9zYlI5OdT86slyzJTNsu3uanT6pU4tsvY2EpLllAiespvXoAUHVCy3RnzyZGILG+9HloVmBO1+UOjzoY25DL/z0N8NVpKF1X2lB2u1kfaTpStwGaOZZwFYdiEyOFDhTpS8Bd+PlGOoPFvzII5ZnUtcLj2/CtTS5z1HMrXcrBy4NjwbuNugD4vDBb4S8SxmZAM4DYBRXecBPC/lXiivNdRQQ1c4XbCCM8/zPMuybYM1siy7G8DdANDGdb7iBtWpGxSJ8BU6tWflmp7w/Vi41PNWKeXpBbx6VXl6ocRTambhuyE8HOdsO230U7AFRGNZ766Ts8myg+qwbW7MZVhY3QcAODT1LE7geoGcD6ai34PPNsau1KziaiWaNOU35rKAYuXz+r9Su7salKsBMFXeq77fSvmqkc75vMeppbhBVczz+9cIYBSbLWdFLpQRvlJjBbaXGPl8N4tlihdZls0C+E55fQnADVLu+vJajfI8/zyAzwMFKMsrY0PieTE41XTqKYmsdr+32C0gsg74alNkQ9XGK7vHdmzofn4Ikbu08VTULFoqsigsVz9yQDJ7l4o3xr4EEIFzNjtTUf2sw4pCXjg9Ha+Gc1MFHBM56fV2N2Zzlxw3d/VyZfCcE7ge73nnUTzxyJujRUKLkJJ6yHKj25gr43SWGcN1A6DlyG6gRNiq6LfZmYqAZVRaKkzeEvugoqT9nlhu66bNsr/X1cRGjmNiCbU5AGIglrodqJ6JMHC73EMgYlTiyURnqnZQbHamCt5/SBoKwVnqLP6jWEP+JYBVUXDuzvP8Z7Ms+yEAH0RhDXkLgH+T5/mbB9XfpC9s0hdq3zzEplWwNukLX/n0hcOYTh9CocycBrAM4BcA/A6A3wKwF8CzKEynL5am088B+PsoTKfvy/P8Dwd1Yif6hth4C2xLn9XnNe8FUEGMbRbxlFnYWkMsKdRak/cCiNroZ18HgN59vbAZ2CRDNtfE/LueC/k67Gajpr/UeO3GrP3XDQNAlMHdgzxzsQDA3vuv6Wtq9GJ92PIKg2f/GKPDOyT4bTDJEMtxc7KHj2ZtV3O/t6FbTthGa+McapZ5zgk3C/Vheu6ul/G6j63VfEN6i10sfuGN4fl2d3VbviHDWEPek7hVc+YorSA/NUzDDTXU0JVFO9qRrF9EKhL1DQrUslGqPKi4dcKyZbUPKbwCn9dyFqwF+OIGUHeAAqpTThP6jMxs1thX1key4C3b3xgRWefUkkmGPrRnqCRD2p5yhp7exOtf774eep+skgxpv9T7Veth/3XOyO2kkgzpGD3xhnXr2PRZHUvQg8j35tWp15UsiFD7oO9GHQW9789bF/welDP2nCBfdWH1vJBlnpLTLmygHqKNNBBfb8QGj3XUjQxA7cURdce/7cekohBRiso+283RpuzzEKvewkiR1ZlE2dJKb0u7KAFEKEcgjnNJsv2hvmTtgRsiZSYXv42V6emUbE4OzqHOq3Ux90Le2fgW+g1EwWNKRKciIr0NwBM9tZ1UCEYPzOUdhJ4XtTu/zkF4Vbmobyf4jZ1Qq+yyz2obnmLNLnbvmg22Oigeo5rxSGqhePHWMwCqRLtcmFYPwL+1Tvu3AtCs3sae+pSxvRORG0O4Vv621zVmKH+zTUt2rqiwnbzjeaw9cEPNauPNa8r6Y+eXOWO1r/0Uz+yfbpI2IjvLAPGJ7YEBtc+R1cnZWLxUial8pkB/jtt+e7ae3mIXT41+fWgX9R2/WQxD58NZpEgnNxWM1XvG+rCEts1JohyHjWcBoPaB8pqKEyl21ppq+0Uo90yonr1fY0zYjGRU7AIxF2E18Ryjkh2HeltO3vF8UMICBcZEI34HfIH4o5A4V55iOkLPyomc2mhJyvnpRt3PUqRz7nEmyh15AXu9g8vWpeOw8+xxjd6mc1EVnFcCeaYmBap491PacV2UNkzd+nyRUMZyMZpkiDiBcNqIeLAxl4X0eMo6KhaBH6nNPWpxEtGLL9vQ+tnGUrnpUIxhwJaNucJLdGSm4GTw6HhN1l45PIvOI8W1ic5USCk4fTLHyoFzaHdXMTJToBwYM3P6aPVeVIxJ6SsiXUpZVlMlqmk1LBgg4AuKd1b1l+90GjFZnY6GqqvVX9bJTW8SdR8O9jllTtZylV9GDO33Dh6N9al+UVZXApRRucrnQggCpJ0I1X2dZbYDymqC3zTUUEND0Y4XQ4YVMTxuQe+TPFHEUxT1U/5ZnYnWY8vpdUVDei7sWpYgGroZq+uzTXjD57R+IAacWTHH4hhYt82iTldrzaJulY9Axapb+T0FLKtlUZc5UZm99dFlnPjm9TWAG+fX6ies6ztQ6WkUmKQcgZdF3T5vORObu8PqrazVqh/GwguVOEipzb7buLF2fqyOxJY9/o1/NbTOYkeLIXah83cKcBNYTGMOjVhEFC/Iso+t0p+ALG0/qwHrUVJWE6iCu6i7ND92umcvl+7Zk46Siguy8C8YCXk2p48XL18jW7ENrZ9jsIF/lAg3Jk0fz2oL+8Y3vIATuL6Aape+DrtOVtBzkr6bWO6fqsn17KO2dWbPuVCf+lSc+Ob1uPENL6D3yEwdwdmNA/QAwMRScU19KNiOwp2DgrRbz6JOfZHOw8TS2Ugpyyzqdsxav/aLcPmaDgzx4cbv1SpgrTmVZVuLXXcTevb2OcxgKiiZN+bGMH/EMf1PXFt7NkU7grNogt/USfveBL9pgt+w7p0e/OayET0IAYRkMvytsSEZG2BauIlWdzWcbPxImdgG8tKWD80VTj6Ig9/w9I+xC7IIxclnojNVOiPNRqf6+nxpdiyVfrqQrQVlUPAbLqrx0wCQh/oBhDbs6W1ZVqCANDPYiw1+M356JKp366aX0H54LZS1m48X/EbHy8Xx0O8dLgPX8CMfCfO7dVfpBvZw/+A3D/3eYbznnUXFXzn+9ig1I+dMuQFrIeGitDEiZha20N1T8IR0buN7rzAkxbfVQ8FpAUDvkZkwB3pIkLO58Q09rB2/IXqHQD2XKblDVd5a0z7rn1iqwgMCxTdhFbkIfWzVuCP2i30fdGBZahScDTXU0FC0I8SQi6XgJPVTQFp2r5+Dj603tOnYq61MmUIDAnWgkYfTsOKWddiKooibNjzAl9cHT2Fo+57CCAA+CE3ZZEbhVtdyJevOruKNpyQl/cDPfCMSSSxew46booKnzLVJp9lvZdXJuqvIoXWpQ5eKpUqeuMA2CWYjWci4/T5S3zDnkO/FInftN7rZmcKxr/4L/GX+4pUPyhrWN6QfUq2m4DQaZNUh2I/fazelD/DKedpq2x6QTgxDd2kvZL32wRsP/+4H//X0C9ZHxNNxpLwoPcsEUPeS1XuedcK+M4ugfOZDe/Cedx4NehBP32HxIh6GhNgWq4u5+dNP4Y/+4WuisqqXsqRZ3jzPaI7XigXW4mL7b9+d6md43UNl0gvWWnXUu5rPP/XHv4SNleev/M0CGA7uDfgOVnYBD8wbIpSC2PJU9xannjApjofXrKOSl9+D9+yYvMS/tn72R82RQAz31g//2dvnaqnwdMzqm8GP9MRn3hpS4ekHbZV8nkJV+6ttsB8k7/0SZs4gwFbhp4o9oFL6WW5BUafaB2tmvvnTT+Ern3q7a6FIjclueJwX6+JuOUcdB8keBp77gnLgqmjW+vlbDQGNI9lFgHsrS2zr8Z6xnMYwHIjV+gPVR8KTIQU59zYhW79tA4j9ZuyYLGfg+TzYevRZz1IF+B6xis/w+mEXnRUXuPn9yHu+HqKGA37SZqAei4RjtX4YQAW1t2KTjZnC+qyDIBexFUVSpI5zapnTTUHny8N0kNSp0Ipn3kH5tbUvDs1Z7GhryLDkxe8cdN9e48tpy32FcQOx7KgbkOIrQr3OBpISpYDqY/bMhpGOIrFJDKp/ELFd+/FZPQRQBtvpdmoLeFLKA0W8yImls8UCAiII/iRia4pujsXYqjkApoyoNhWSMGnU8N956O9ifX4UWze9hDN7qrdCjmL50FhIPcBsbNNH61zmvodP4pkP7QnxKnedzPEDP/MNPPHTb47eL+tT3Qth5L3FbkgOlMJhsK5a4qpbDuLEbdfi9Z8tLxw5VisDxH5EIQoWEMIK2jZaR47VrFcjZ4dnFhprSEMNNTQUXRWcxaUgj53uR2onByqHoUhcMOUtQEeJJzn1DqoktUpH27Zer1leyqC4DGhrAwTreDcFc6I0fnoEm52pmnxPXca+bqeYg/JUs7EuSa3uaoRraZUn6Pr8aI2F10zhdq4ohlAkGXv6OjeKuYW/TyydLeeymgs6DxaetnEbM6jeA9+Lh21Zl/nlOC2tHJ7FpOifrCjx+m4shpDoHMjxbHamYjd90xf9Hi2H4/WrH12Vm8WFiCXKknq6gZ4pz7/tQiULaK/b+0AR3LSuQyg3k8Ozrvyrz/drI4KEAxEsHIhl+ZXDs8GrVkl9JorfVZ38OIlw1GcVLKVE8cqDwnsw6YklD3BVbFYTsiFSJCnMqhV6knI84d2hf0eOYdd8jL78gZ95Ck8svLmmR8Dh2QixOv+u57CEvaE+RfTazay32A0iWAW6Q0i6/cyH9lSKYlTiEAFXrW6s1Ax+M45+IoDPTPT51pFj4W8FNGbffgnD0lWh4LyUZJWl/coBdYyCUj8HN5KVbb0I0P2Up16f+pUJ1wdE4LIxNajIsyhUD1eiGxv7ZZV4a5Lx3dP4K9lAzIRrjz19Hc7sOYf3vPNowGGoNcbqXixcvLW/E8ygaj1hsOC9918Txrt0z9sAwEVJ2nnup7NQMyevU++j0bitVZBzE9owUbCs1dAL/NTav71IWVclZ3GxydqxPbLX7YuxdVlzVyAxdU0snRVIdnx9UP22Xs8JL+o/qk0gcCr7qzgZG3NV3pCNuSwo3CL8RnlSWlAYIfakIAaZ3BjkEtbnq1N1qXTAsr4a7W7lLEZlJhMRKTR8YXUfWg+3CpPsu57D2gMxBHt9fjaMYW1+FOOnM7S7q3jPO7+Nh0DgV44ze9pod09HqTNZnyemRqIDUNuIVUTwklhpDthwrXwnpJZwvh5uyMXfGGDiyGgTz6Khhhq6yNRsFgMo5b34StdX6B+KE8HLlHWh9W+X+vVhEPWDoF8o7TpZQMwXVvdhYXUfDk09G+4tLk+HbGapvvCUZ6rFfu2wvguZ881Sh1RwVNX4berDFG3HGWy7jmOWdoTOoslINjgjmb1+qTKSPXfXyyFY8MXMSKbE+5qIR+/ZOqy1SH09rI+LDWIEFC7yf/QPX1PDoTDDmLVEaBoH7b/CwK1C+pkP7QGAKFao0rc+MYn9MyshTKAVF9RnxEs4xZQMrfe3QtsnPvPW4LMS+i/9UX1N97ZrceOHn6yB3449/xn85X9dvjoQnNuJ7u3FrbBKNBv3QtvwMqApFp9lbRwHGyMjdZ31BW23QQNqdO+9918TwbX7+awoxHcQwo8bkPVVYBYrTzFnFyR/28C/dMDSeBhUDFoiNF77qrBszyHM6kIshJvjU2AYUEDcGVNDyXIVFnZv86TYDdPmYgF8hK32zTuk+BxQpSqgkxuQ9h0ipXyDrOOb3Yh7i6/C6N6XklJh+YZ57nwUnJ6Phff3+So4U6Sbo/WnUYcwAC5E2sKoSZ7IoeY9tmURnAp7txYHtqc+MAAixykG/F1cnsb+H/9jrL33Ztz78V/BPR//gFs/+0orhKZ5ZL3z9z4erCBn9pzDr/7w53HPxz9Q4/zYJy86u+eroWZpjks3Qm9T0bI2CBIV1Z43qo4dAB5/4cGrI6ze+dIgc+cw1o2Uf0XLcCup55SrUf+KNS6UxSq0X0vAOdRNR67o7705cEPTic2GH8aabCxeX/ttINNHTwW4cLgGBOhwNQ+jtTii6xIyTrOSe9yZQr3Z1szCVCm6iQUFiMyHSee80nLQ7q5iX7fY4Gj1GJvLwnP3fPwDuPfjvwIA+ODnfxIzC1u1nCbETaw9cAN2IT5IW/s7UX/uOV7U94vvf18ow01gXzc2o1siBL6NOEQfQK6oCjNIrAzHaLkWdanvLZaRzQOeo9po3PAJ28ii3ig4G2qooaFox4shl8tFPSV38qRMuajbtPap4DcahyHFNqo7umUnhwl+Q3HBCzGXEiWsSKTss1Wkqd6FSjpluVMiRKjDJFrieL14JJ6Lug1SY9lxJRsj9XN3/7sgQqSeVz0CdRY2LJ7GagUKxSL1DZ4ug4pQIjY9BKaXV9VzDdB5sUhcq5TWe+qS/63f/BdXh87iSgp+Y9vSF+al1wPicPL9FGKaJMgqBFmu3wfC8kA6MbJaiRTm7C14rdNz2R52rnRztRu2vjMtR7K6BpJnDdHUjKozsCIEv42le97mRq6iEhiIRT9VXAJ+usY3fenbAVUKIKRc0I0VqNzgrdi1fKhICsVNBogDFOu8trurkc4FKFCumlwbKL6/b/+fn8bWc1dJ8JsrhYaJmXEhcO9hc1B4fRqGBpk+7SawHZu9VV6qJcZyLh68W+sAYg5AOTQb9AaIN2gvb8jP3/fvg9JT55m/2V4q+hXd2TUXrRdPwz7/4q1ngsXrzJ5zbhJpa9Ui6SZs871qRC97oHzrE5PY/ej4la3gzMZG0Xu7n0jlSksFQG25p9mPcp0mFlud9az+B6oQbp4WvBYzc8Cis+2y7ijd4kVIBcCI4lR8jp8uRIiJpepE3ffwSaCMBaHvn5vl1k0vhSjcnUeqfkx0piIlqfaFxPo8T1RVet755btD3Ww7zG35zahpGyjuM+w+aWZhK0TEAoDdj46HfgQu6NFxLB/KogRIAEIMDADRe9iQMHkcx/r8bHiX7BcwixdvPYPpowxdWClJdz86Xpar6j33S8NvAY2Cs6GGGhqKdgRnkaImbwikn1XeEJ7eV0rekHPL7cBVkKaPFw5bw+YNObe8Z2DekGCW3t8JcTp0nFaZWXBAU7jzy3cD6JM3pHQX7wHYP7MCAOhhxlXckhPbP9OrOa2xXfaJWeba3dWQ0FhN3ax/+milE6nlDSkjfe1+tAJfTc6sAE7ekOmjpzB5Rw+9pZlQ7/O1EaSp0VkkyFOQAsPhMrTsIOQdn/WsO6p09WTlfvqRVFuD8mKm4m4qcIjkiSGeviEVD9KKC1bE1HZTwDQVxzbmshDPQrOBcUwK0+dYbfyJmz/9FJ746TdH7L5iXsJ4P7qMpcf2BjGiXzyLfnFcCcBSxaW1NAH++xsmnoWHxdENq0kF0KQCCH/bj8wiLZtUAE0qgKsmFcDlpgbuvXPg3jaeBRejIkY35rIQAAco41m8Px3PwsM40JdEExmtHMijDPPLh8ZCfd4GYq1anvI+hZ3guD306jDt9DsUtY3t+obsaJ3F+ZLqAFL3dcKA2CGNRN2HaqXn7+0OxfFovfpCKUtPQ160vERaMIoM67Fugn2YWKpvBKxLZfVpVB9iyyxmBWcBxcm2fGgOkzIGau8nluJTcNfJPJghK2/LagzMDq+WFEsaDZxlgUq/oNYF21eG9aMvB6Nwb8wVfZtZ+G4IXAMAr8dp7DqZY+mxvQHCzQWlm5CG0tMAOk888mZszI2FfnMOtD6ds3bQl5XvwdlMlLwwfDMLU66YqZaTfQ+fDNwfXdo7jxTvVyHipOpdlO8KAF74et++KTXWkIYaamgo2vFiSJORrMlIxjFZuHeTkazJSHbBNIzXqYd29Dw5lXSiU+X6BZpNKUs9olLPBpVV4NWg+r02UhnJrIKRZXu3HPSTDDlJhbmJhiRDZf9TIqH2g4pcuzkq4M6zFNECAiBsHC/eegbnlhNJhkpSJa49OKaPngrBYzgOJhnSQ8uD61vxlvV6ugXPwsH5Z+Jm1mVBiToPVr/BOm0b9j209m8vYO+O2CyaSFlNpCy9Z+toImU1kbICpTYLL3KVNUtRyWXzUOpC63cCKKzbg4Grcs5blHxe2Vqgss3zFCaloOzeB6KkCj9ritQ2UtyKLlDCj63PifUzePHWM5Hzkd1YPC5ET0Td2JTGT4+EMXzrE4VKlVG6UuIbFwuAYIHQxcZ51L7o8xTxFGfBOSRUmovZ4jeUs/mnX/kygAImTsuJio/kpn7+vn+PD37+JwHEZtGUZ6t9T+wj26dI2frocrDoWL8YoNjgb/70U1h44zVRcic1Cz/x028ObfzhU58bmrNoFJwNNdTQULQjOIudiLNoEJznh+D09BMW1XihCE721+vj/Luei1yzFcEZZf4q+2r71vrocsBlKIKT+iqKC/d+/Fdw55fvxo0ffrIW96OfspKkSmgANcV4ymVfvwOPMwkciMOp2jb3PXzyyvM63YnExZVKDZgqT/IAV/a6VybWjhc4B6tItIs2Bb7yrqe8XVOafQVl2aA60UKnx2ipiScVOA0/+A3bAEpxDgcrHAoX2KG52kZh+6wWpCUUG4XG4Jx8sFhkv/rDv4J7jksMzv2dyDNzYulsAeHGyWjTmSjFjPkjFfL0zi/fXcTgPP6BWt9UNGY/UyL2DIq6w6Zbigv0ytU5Yt2WdL4pRjJ0oYrdLOfF2xiGrsrNYhhrCKmJwVlRE4OzicHZjxoxZAAN2ngudX2eSbcfQnU7G6XSIJOvPuthPUgeWhPwWWag7t+S8nVQvwwiFpkywEbr0nq9U13LaOAallEl7aA6gCKADjcMVax7sHfWQ2vW5B3PY+mxvRGuxjOXa5/7KUXtpmqxPVZ8vWpMp9u1hqRCz6lcaE8nlfGUbfOcrKy9G6jwAfakBYodf/z0SHR6eSdkP7OkZ0akt2QVTGZkoB+BjjfFhlLk0ETD1rTs9VH/VrOy1mtBRnR2ItGq4I2Z754iwPy9j7sgMMA3ndoNgcS+aj9Ytwd6I8iM/dX7P3/fvwcA/LPXvKGGTSFuY2RmMzIVq+6GuhCWt/P9T7/yZbz/D34CY09fF9pOmYT1PdKaRX2NmoU3O1N4fPGz2FpcaqwhDTXU0MWjHcFZXKiLuuUKLMrNg4p7ikYPocnrpH4sumc5IQsNpHN8elgBq/32ArcMgqx7/VXOaVCE755RVvbLvNUPzKTtKSnuRRGgHgzai8LtBbb1OELbD6Di+tSN2yJIPU7K9se+i5/79jdDTE8SxQovO5zCs9muBeKplUoVzvP3Ph44EusZrNHHOTYdP59Z+sy9V0d07yuJrGWjnydq6j7QHwIO9Edppiwvw9AgBKYHyBqmD0DsN6K0nYC9qTb1uhe3YlAdNn5Hv7GxDo5FXeV186DSk6kIAdTSLDJ4DoBow/ZEUhusmOKnrVPr0L6S7IbVOnJsW74hA60hWZb9GoB/AOA7eZ7/nfLaxwG8H8D/Vxb73/I8/93y3s8BuBPAywD+lzzP//PANhIBe68kBOcJIjhNlGVFVyr304OP4EyZaoNcfWguieC0GAUNeXchCE67iDx4e0BwmkjX7e4qToRFU+lYGLawQnAWliDvQ293Vwu48huKEaUQnBOcW3mP2t/lQ2PB2kE9yq6TeXDu2j/TAz6J0L6H4KRu4p6PfyDUp9/YrpM5fvH97xuM4HzgBuw7mk4BwT4CiHRik3c8D5SWHtU/KQfxpi99GwtvvAbLZiMEpvCmL30bT/x0OU+3HASeii1V/WggZ5Fl2d8DsA7gN8xmsZ7n+f9hyn4vgIcAvBnAHID/B8Dr8jx/GX3oQjmLFKah330vqM0wEbG0vFdPv+f6nd6DytpNa1D9/frgkW6unkJz2OjeXpv8kBlzAUBN2ecpFIHqBE+F19eES4Pg3mphUCU44d67Hx2P/E08cUfzewAIymYlijifu/vfAWDU8DhvKTdJbZNtkdQHhH5GKs7pIXfiM28tnnc8aYF6mgPSM7/96YsH987z/GsAXhymMgDvBvDFPM+38jz/cwAnUGwcDTXU0BVOFwLK+mCWZT8B4A8B/Eye538BYB7Ak1LmhfLaFUdq/1cahFOwYoQ+zwji/L+fO7yy/IBz4jjAMq3fth0QnKkIYvs7NQWcKoqt0jDcU4XwkWPYdABINiwe72YmXQAAFnpJREFUAGzMXQeggiVDFMQqWhCEtmt+tnZaBhFI0KkBDOcMcWLpbISUJFEsC4rKklOIxNqyrDWZsj4tu3LgWux7+CTu3FNEDf/VH/48/uVnfzjqy7nlNkZmNkPkM3JtkQhVjm9kZhPTRwsTaLuLIOoGdwBgqIzr3dvqJuSRs95M+XS+m8UvA/hFFELoLwL4FID/aTsVZFl2N4C7AaCN6/zODWsNIeudsoZw4Ys1xGIc2qjjAbx+hOecMtpWYOkRs4Jt1tGZwlLQpaTh1l76Qmv9aJv2Ql1G+QcUiEyOw+qE2t3Vgt2fr8qm0hcGoJTOi6MIBaqFpEhEQqj3dTvRfNn5VTARqcpZOhq1AyAgY7UfhSt8fSEVLue1acfGXBbC9AMV9F7TF3IeCpoKnrXTx7OQqIgL+F9+9ocjnERxbwvANeCGGawaEqCZG+brup2A03jdx9aiwMWdR84Wa6Msu0n911evx/oDlcgyeV8PN37/kwVqVvRym1+PQwT0o/PaLPI8X+bfWZbdB+A/lj+XANwgRa8vr3l1fB7A54EClOUpOFXh1bvlYPg9MJ6FLCYrw3Nj4EKdQfWsLgjWafH1AXjUjctZfUGhsa78O/i8/s9FqWTnIASQsQ5moX6ENmz9SuyDnt6MxahzUwCyig96ojMVpRm0G5o11XqKUDtHOo9bd01is8wiZt+n1rlrfrbG8ew6WUdJpmj/zApa3fhTb3dX8dDvHcbrcbq22XgpEFnPZqeKR6E0MrMJAJg+WsTHsHP1/j/4Cdz3lt/AL97/PnhUzeVoqFsPqZGZzeL5zvvC/ZGZTbS7axEXxYPyW8uTGJOYouG+cIPT+zvYPf6S2x+PhjKdZlnWAfAfRcE5m+f5qfLvDwN4S57nP5Zl2d8G8AVUCs6vAnjthSg4X21h9WwEa6us7BcNy2402wmrZz1Obf12znqLRZoCHa9naaLlpp/Wn23whFVfEvv+SAxRRwWh1um1Zb1madbUAEUeFgIAnrvr5aCMTeEwUmZKEq1mtKZYfxLFSViPXPsdDPIgVgyNhZxbxOt24lkMYzp9CMA7AExnWfYCgF8A8I4sy96IQgzpAvgAAOR5/l+yLPstAH+KgjH4qUEbRUMNNXRlUAPKukjUgLLSpKkUlKzfyXaympEs9iblVGZNp0AxPxZ3kuJMrO5Hnb0Y3i7UW0azokn23HK7BqBSHIZyACpO2vikJIs4VfOt4k/sOBi0WOu/qKCsK4EuBc5Cr/era9AmYdv0XNf1b4Wbe2W9vlrxYJjxWmodOQbs7wRlHhDjKSBiBn975C00lcdJG3NZlN9Cg+dsOo5cFv9Bpef6/GwY52RpJVKLEMsXepIYqLbZmcLe+4HlQ8QmFPV7zna0UuhmRJd+uuIXdZzCs4/txb6jJ/HirQWWotBnxIrED37+J/G5u/8d7vzy3ZGo4IkZZ/aci5zOqBTmvFhRLSX2MZdt2PA6U9sCZV0Vm8Ugl28XgjzktX7xIIb5be9pfdupV6/3bjkYlFT96vfq67dp9Ba7IXYFqd1dDXEngkkW2+MuqtO/+oAnXLV31ab2idYebixcXHZBBCW4XNN+qgkUKMyn1kvWW2SbnSm0Frs1/dQMivom73g+BOIt5nkOm50p7H60ChYEbNXqZQCdD57+ycA5sI/aD9WrWJRuKON4qtqxk0JdR44BE7WqknRVbBaXki5mLIvt1GcXvZoUW91VF0cwTP2DYl20zEJN9ad15Jjfh76cRUGpFIxBie20OyH1qjizMZdhQhSBRVTrucihipsoFzj7MH30FM7sORctsMINvS72hA1a+sH6lh8bw/yRx0M5za5W/F+NjeICRYgPnv5J/Mk/+bf4/v/xztCHCBZu5gb7O9FmunJ4Fs/dNRlMsnS5r0Sbamxbd03iWUhk+lsOYuQbX8ew1OgsBtCwwWqG1Vl4YsUwNIxIYTmLYcQqrXOQeOO1nxKBLKX0AF5fUxyetbZ4HrkW6u15ifI3IeQKn/bigCr8W09leoumAt1oWwpbB2LvUAD46n/4VQDAD739R8J8KVfgpWQAKpFJYegp72bb1425DM//0qea6N4NNdTQxaUdz1kMg7OwcSOIF7AnLSM5W8iz9TQFqt3cxo+kEs5miLKejt511mfDxPGk8PAQKdg5x857POUUK+Epu6jgYhJgnkLEiHgYDs+xTE879t1mGuc1RtS28+hZXTwnLipZLZI2Fc3MGwND5WnfWL/9lmzfdB5TfSZ5CGBiUmxKRc/69H9/43fw2t+/A3vvv6YW44LPkZTbsONXt3agwplY0e/x3U9iY+X5V4815Goi9cyktYAKtotBlfwff2hnbtsT7itR2VahPqvf6/Oz4dr6/CzGT49Ei2djLsPIzJm+fQl/lxtqBbU2QWKkvIbv181YRQhdQK0jx4Db3hqsKCSNR8m61K8k3Ds8G1mFSAquU9GotoBLPYYHrtL5BYDX/v4d+LN3PIDX4g6MPV1aaVBtQjYUYW+xixUJOsSNku15mfciV4hGZ3HxyJ4y56ugtPUNMnHaE105KwC1E8Lrr1dvP52FPcGVYwFiWdyL7WHL85mk85qQpu8DYoWgrcOLsQkg5F9VBeiZPedC2j7NjGbT/mk/6HuisSAYw1K5No1i7lkjSB6HqFyMRVU+d9fL+LN3PBApPUneZnHiM291Y4yyn0A6d8nxb/yrKytSVr9cp3aRWAWUx8pxIXo+CXZBeAsEqIAw3j3LDitGYOmetwGo8jd40aBYh9Zn2WMCbVSk4jywT5GDmmIiOHeO0nAQ6Mkq0FSc8jYy+7zdSLzExrYufTf2b/v+eX8QXiU1Pi9uhJ3HfopiKw5oOED2jeU0zoYX+tETf7nZzSxsBaXn3/nX/3PgDKz46m3kTEJlEyavvffmIH7yme1sFo2Cs6GGGhqKdgRn0UTK8uvVE86erB55nNSwpG3Y56zSr9+p7r0DzXhO4unZr04dk6cQtikTrfKblKoXqCuVeS/FUaYcA5Wj4NjorEbXciXmPmFkq1TIRgD4k3/ybyOTqoaJLNzUv+uaZNV5rt1djaDtfAfbCdi7IxScFxqDc4l5Q8wCJutJd/T5e+vsb8gbUj7rtU2KlGllecq2XoYumzdElVraVrTA5V5b/tc4Ga0jXTdviK1fScfr5Q1p7e9grVw4W3cVMOWQN6Rs3/axd8vBKFZIq7uKpXve5tZPuDXncbrU2DObGGN6WIWfzg0XD3N7BHlfNlLPmlHkDan3h2z6Rlnvs7fPFV60nalYhCvrP5HIGwIgZFf/Z695AybKvCHTx5k3ZK2WN0Tzt2o8C83sxoX+Q2//EYmHUb2/N33p28A/LMZMCDhQfCPLh8awddNLaD8seUN+PM4bMtGZwrfnYhh6P9oRm0WKLLiEv208ixCTwsiWlEGJ2lM9hjrj8Nn4xK9OEIueLOJZdKLnq7gYRdluGcB3Ymkr2hT0I/TS01kiCo/9oi/AyoFcTqWt0PdBYC81p1kOZ2LpLF689eWABlQU4LBcik0twHrZHlBtNls3vRQWTz8HOi4qhYhbpWA/uvENL8D71Gl5sn1WnxVbT++RGbeN9//BTwAAXre/iC9h6wzxKCSehfbfArmAmMvw4mH85p8exOtQbAbKTU0DePETBapz5fB42ffni3tiKm4DFz+exaWmi5WR7HJG9+bmQPKie+vYgFh55+UrUVIteCq6t61fiX0+n+jeOqc6DquIZCYujXFBjIGSBtSponuvuYpP9v3Z2+dCprS9918TKV5JVgluyYoLVB5zbrl5eUr0ICbdV0UYV+V1LZDvHc+HfKv2u+TfXj9VzPBEiq/+h1/Fa3//DgAF50cuSzeZyTueD5ngSUHReV8v9Gti6ey24lnsiM3iYptO++UC5f2UKcuTl3XzsgFiUhr4lJxLsgvv/2/v6kLjOq7wt7Vsr+otbK0tslfEWhRT3BLstK4SEjdQLIop2I8mEPxQsAtNoBTShz6kj/JDH1oIggbqBNTYFIyhGNxg2uI8xTGpU5NAqVvXXlaq5UiNHESQXCndZvsw98z95tyZu9eObW2k+V6k3Z07f/femTPnfOcc33ehYCwAMlaPUBtsjZCxM/FJk8BCJkpdv8yFLASyk2rCGuDXWQDwWhGkLS35aWnSpzOShTuUL1WPgSnbgGu25UVEyGpiURDooDvSB9FFcEAbnZRYjmv6GQPgbG5Sp/x/bbyKf35nEoA5nshcyULK8US1fkPnoY3WkIiIiAeCnpAsYmLkmBh5NRMjc3Cee02M/NQv/wwAuPz4Bnsc06QunStFoKVXH8HrG7+7gdN/2+skRn7jrbMYO3I0wzGa+enThoy2+yb6fmB0NdfGq/jqzxZSJS7Me3Jxw+v4+B9z6/cYshbRzTzcywj5m9zNWEKerr56tb7D9z2QzffKR9OQ70roWJZH9/YR3rRfjfZ8BVJWK+uktBn9wqnXMHbkaNDr1SGGvTSH9vFBp+zdJBnqaWvIveJ+6Cx8HIc8nUW3OBECH4OPH2if0xsHuwmNzacY9O2yms/g4yzkMS0ZoksA3BR/2h8hw3totjKxMHyMW24nRF2XXRxwWbMCy7AFsOihaNeQQnQSNQBtuKZaG5xYWbPkRWY/Eq4PMNaJtow5GRs/R9PHqg4PYz6RGmtXzOfGuf86DFwe79iRo7hw6jU8etqE6BPKu/WdoWdsfvIRYMhVuD6MvCFrHvbhBYKBZnR5RjcFZ99Iw3IUnBc8qUcUgrXEsYl3E71g5LWxkJg+WcG5MLQJlcaA5VUAwHAr60DFPAqNxaFN6HuzZTkKlZm0vGjxZZHSmb4FfJzoa912FjVWcHIdMjdiJm4jPRKwMldHvpbPOhamm77wKWehEdNq30jD+GHAzSnK9cl9k6RBRtG5zZbRjoAcM3Pz+1/E1OE0eE3t4geoXVRHx5ZZ4BeHNtljIs6Y+/Do6R/ixrMmVeKTV57HR9/7DyoqGnm5ZSQUE+LPVXQWRVRwRkREFMKalCy6eTn6QsLZ3ZQkAmYRmr9wXKX52jwyVDdqt6QdbCPdoZjx6BPNi7ZhTXb0XRruzSU6dYPbhumPNpm2my3LpJU2K4mC0kfYYsg8MpFO91n6Ye8THWmknaruayLFlVtZic/MT3qUEompDKBxbsD2S66RMYiiWN83kapY2V1u3U4zhSUQnsxyY8BRsvKzJNfrREFiHm033wP2bceuiVk8eeV5AMA7P3/FKj2F6Sv17pow0sqFU68AAA5cPYjO68WIbUCPLBafle79IEhZCyFSltLUy/XXNSkL6E7KIop5t3gVHIdhy60OauR1WoiUlfxtJol5ZDwspoq465Kywn3i2A8CdpeW+3D90EYAHcx/0/hK9M9+wY6hecyklclrBzCWCMPEBOYnH0ENfkWpZouasaXBekRHIRYaPoZUB+dROT5oFxLLv6AXXawel158wknfyM+JWC/+9Itvp3OV6HMWrHJxwD6v7A7Pmw7zNKQtJnuJJY7HIDqMsSNHMT1exdbzsPVOHa478T4BoPMlf+pQH3raGlKE/ORTimkXdG3e005TPmelULtFHMUAVwPucyUH4F3EGGKKzDMLS7mQizn3B0Bw8WXTp+xkwur05TqVOrvFcZDvfPPIZlytmOR75jP3cj+LmE4lfoUTmuClOWtaFIjpkduTvrUObcxE/ZL6fEpd5/k90UZzruYE1g0RsthczXOwsueOowiVcTGkPlF6SlrFZ7/+F1x68YnM3L7dnMBKc+bzYw0JSRYL9DCLYs4nWXR7oVirjZGGsyPKqt5utuxuBaRSTcZk13BFU82z4B3G2OXrGZ6F7PJa1AayKQjZd0XGVD15Cdi33Xo1chshRzJ5oYRbwMccaZclCgBYqte9PAtdJ9fBPAuW3obPpOVsH0fr9t6EXh45kvDiUD15CYOXs5wMvTgJ+mfrGVZnuXUbU3/cgS37Os4OXm227PMgqFE9wljVnB1ROpbPmFynczQPy40BLE5uwmYy00pfZdGWueT5Zh7Kyp47VhEqdezcfRMLVzi1MND3ZgvT41Wr9BQp4uzoM8CoqWfkuWRsjQGgXVxtGSWL+yRZhKjiPvdybg9wzWg+nwh9xOLfpe2ieTF5LnwEID023w7uk6p83ATNKchzUdfxQvl7HS8UcHOXar8aPefcBy2x+LKB6fiV0kboPjL4Wj1eHYOT62HkHavZomR1Jso3BDAL/o5XN2QC6Ohyd8OziNaQiIiIQuiJY8haAJOKAHItB6xSS5OFZJeoBXgSDLacAHIMS12+Fzwu28ITAfUhzx3eSjJDrn5D913Aklgoo7fUIzEsbH2ii6D++OagMvMJluqbszEkR+uOAhIw1pewzuKTjBQg8R54Tsw817MMzP17bYRwAUspciRsnGM2aColVclblaElY18U7nIyhp27/4Ua6ViujVex49W9zpFSJNzv/uQGzo4+g8defgFAGkBHW4RK212dRx56+hgSkUL7pDwodGOjFrleQx+7RCTn2Ard4POAlZfM597vU676jpnssp+nrNXHOSDVa7G/hS/xEKdDkOvZz2Rlz51McmYAjg5p5Ln37BhZWS0u58JM3TUxmwnIJGN5462zeOzlFxwd2ts3T66vSFnrIZ6FRPvC6NMPPJ6F2ObvJZ7Fddp9pYzZdbcBEMe3dJ6av30cQPd4FtcPbcTO3WZ3XpxMXffLrTSVI/dFFNEiXel5kYVj6/n+1HT6/TbaxwftuBwqe9K3UTKdCobP3MI8MUxrFz9wTKe7JmYtF0MkPJZ0plBHZYYlEgORPLee77cRrton2sCk+X3qcB2Dx1eAIVirx3JjwJpHV/bcweKt/qReo7f4649/hbHLqen0038XXwKiZBFA6KXtFudTl8sja2m/lLzy+mEPRfHu1lbouKB3aSmrlcPcHx27IuS34gt3z9dI+3mLhf7suw++REuheeBjIy8KfL2z+NA1gEvKkt8A1wTsS5Tk+Gp4zOs6SZRApDCmvYv3MW8cTMri8jwmUXoeuHoQ7+z/DT7ufBQVnBEREfcPPXEM+azo5r7t+90nCRQxkeq6fG7SAIJBc9nkxtf66mUP0bKqA56dUx8NuO9Sf8gxTDgkTp/INMvZw6U/bWrD/u/pVxvuWVz6ys502hvWx+1wvmtkw9L1JUpFrXdhb1pm6C7VSxhupeK+ZEITL18bmBhZZz3La0mkL/ltqV5C9WTWkY2dAmW8lWQMy/v32j5UacycWrKSHKccAppHCjtw9aAzR7osl/nD136PL292Tal56IljSF6SIZ9SS7MXffZuH6ORy/rSuvk4GbptwA1zphcAKZsG7M2yErmPALz8Aq130d///UfbcsPIa4TmVL/A08eyAXt98xiaL9+9CC1iRpu/wSnrq0OzOyUatk5qJL9l3PJPtDNMTSANiqP1SBwBm+dO4kHwGHjeAKN70UfGcsvEGmUWpW/MgD/oksyVXG8X6BOpjsWONdG7TR/7Hz6dK6desQlbVSuEP9wygXffX/78BL8plUofAlgCML/afXnIqCGOeT2gl8c83Ol0vlKkYE8sFgBQKpXe7XQ631rtfjxMxDGvD6yVMUcFZ0RERCHExSIiIqIQemmx+PVqd2AVEMe8PrAmxtwzOouIiIjeRi9JFhERET2MuFhEREQUQlwsIiIiCiEuFhEREYUQF4uIiIhC+D/6uMRPO7llogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf8986e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 7.080430507659912 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 7.0} is: \n",
      "[[16.  8. 16. ...  0.  0.  8.]\n",
      " [ 8. 24.  8. ...  0.  0. 16.]\n",
      " [16.  8. 16. ...  0.  0.  8.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 80. 56.  0.]\n",
      " [ 0.  0.  0. ... 56. 80.  8.]\n",
      " [ 8. 16.  8. ...  0.  8. 80.]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf88cd4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 7.857206344604492 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 8.0} is: \n",
      "[[18.  9. 18. ...  0.  0.  9.]\n",
      " [ 9. 27.  9. ...  0.  0. 18.]\n",
      " [18.  9. 18. ...  0.  0.  9.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 90. 63.  0.]\n",
      " [ 0.  0.  0. ... 63. 90.  9.]\n",
      " [ 9. 18.  9. ...  0.  9. 90.]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf88e8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 8.728142738342285 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 9.0} is: \n",
      "[[ 20.  10.  20. ...   0.   0.  10.]\n",
      " [ 10.  30.  10. ...   0.   0.  20.]\n",
      " [ 20.  10.  20. ...   0.   0.  10.]\n",
      " ...\n",
      " [  0.   0.   0. ... 100.  70.   0.]\n",
      " [  0.   0.   0. ...  70. 100.  10.]\n",
      " [ 10.  20.  10. ...   0.  10. 100.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf89a0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 183 built in 9.403308153152466 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'edge', 'height': 10.0} is: \n",
      "[[ 22.  11.  22. ...   0.   0.  11.]\n",
      " [ 11.  33.  11. ...   0.   0.  22.]\n",
      " [ 22.  11.  22. ...   0.   0.  11.]\n",
      " ...\n",
      " [  0.   0.   0. ... 110.  77.   0.]\n",
      " [  0.   0.   0. ...  77. 110.  11.]\n",
      " [ 11.  22.  11. ...   0.  11. 110.]]\n"
     ]
    },
    {
     "data": {
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Ahgwg9k3Lktux1h7Orw1LYK1QSe4qYYngO7PWJ0t2zrStYZW6akmzlhLbryvSRV2T/pAitk1k12LC40hY7e4qxk/PhWdVXODHNX56BGf2nAt1kyaWzmJjbqyGPozrqpSieo9/ezJiW67bvu6arwBc64LKswveLqoIqCV90ChifGaXUy/7pxsF76sugGV1k7Jk+8rxU95X/UgxzmoercmPrL8VAQjGg3NQpPQHlvQdaF/Zlm5C1GH0TFneJ1kEpupn+H5aRpTy+mFJ76tOKzo4B1hDVD/Sku/H69dlTwWwXWJaQLuD6kKnjL/ZmXJNe5udqcAxFDiLehm9b3EWMwtb28JZkCuw173gNN4pqmNQ5Wh4+X1wFl4sDA8LoLE3lBOz4QpZbw+o1e2ZG8PvbhWjoofKxm/xED04OAvE79ealXVhhD7IYtVNJNVfnRv7bbXkmq1fsRJ8PsTkMDqvGgfRR5+jc5tSK0acjtGzcL5CgBxu5F7IPUf349FVr+D0cA66WDqPfLe2SEibnSl0HvluEFk6j3y3auOWgxX7f8tBtLqrESaD9SnoSxe+npZLpQhgxRgSFX8AXFgux7R5y0H0gFq/iCMJ5RJtTJf/azAfLuCejJe/lwyGpYLNx/UGBaxeR/wedNHZAEOt/Z1YAWesM5aj1GthnhPAI0vP3j6H+XvjMipuRJsbKlHCclVL97wt9NcuMWs1UssH7y8dGquU3RisnLWiGBWXei225sgmXH43usFyTOx7a38HZzeeTLZvqVFwNtRQQ0PRjuAsUrQ+P4p2t/5bT3tgqjSdxmaiqowqPqu6KIbQ9Dl99FTNdNru1nUOK4dn0Xmkkitj02mloCQoDIfmwmkSycb7OxVbbmRLEk9uazptd6V+ILRRw2+Q5ZbTuTidygaOHItOv/X50Vq9NJ2mUJT2ZCxO8Wq8vcVuzcuWNLOwFRTF8/c+HnnXqtjAeV8+RKVyyYL3QabadrTfrJf6Bb6z5UMFBxI4DKMbiPvbjeqrTKrdMA/6Pba7q5jB1LaQp8pRUVmt80sztfcOUqZTy2W1Nq5AUFbryLGKPSoHP21kdf5m3EygFAOOZ9iYQ83qQNZ3+nih7PXEl+njs1g5kGNiqVjowZZfWjm0LACsHMiDl2vxfBYwFpZNZJxQ6ydCb0yLPdjsTFVyczkPAXxUluO4+fGRFLzEj399fraGyZhZ2ApWl2l5ByqSaL1svw44q3ty9m45GC3MlcOzmCjFNPs8f9s56pVilpbjuBmWQK0hw8SkJFuuOgB+C/QQBRDVr0pAjtPOuRLHrRuLUqycj71KlTRgb+jv/k4QabV924Ze3+xMhf9JLadfV5zOoh9Zl2D74RWKzwJAxV1/YikGPpE7efb2OWzMjUUTNn30FCaWpkoOIY4RoCZN/iZXUXErp4ASSm4/JM/cSx2HurMzuLB1VFNXfM90qspUNfNq/hWaSVUP8eztcyHWgbVQtPZ3XCVtS7gQLghaUABj4jVoUXJI0Ydb1md1HCHdgi4aRz9SULzRsI5hSK0hihxWi4yiXlXpDtR1FrqwWS4Clkm7w/Y18g0p9U6RLsR5TxHy03CE1trUW+xuy0V9x28W1lxkFXkeFkKVVvZE23UyR+vIsSjS9Pr8KKaPF1wDxRPV0vNFt7sIog25FUB8DzpTUT8oQqyXp4me9EDFCVnLCutUZa4u4IAGLJ9hG7Z+oDqRWW/rSDeIS3oyrxyerfKqSL0BfdmZqrTvjva/XdYRxZcgB+MsdirgNGGOcjCqdF4ruaAUrmQYBKeXL4McgxUNeF2tJB7+QsmaX+1C9XAwnhjn5SWJ6vZwIaYOvrdpxL4gk6bsdhGcVyQoy2OHAUQmTmVP+923ACUFdnlmR4oakUNPucnYdrXvHI9eV/MlE8hEi9exsngmUp0fe53jJbEdbkCD6uUJ6QG0bKYxPmsBc/ZvW97rf7+ynoWM5G0cnp5AgW12MaoJ1xsH+6vkZSQbZryev43tq+UctS9ePhO2befRbnit/R08/sKDTa7Thhpq6OLSjhBDsrFR9N5+sLZ7WzY1/HZs78oBtI50I8Qnibvt5INPBDEEqMBam52pgJ/41icmMfZ0Fikz291V7OvG2IqVw7OBC5k+egrf+kTBQO69/5oifeLJHBOCvbBQc/adp78q9KxuBqg03aq0ZBv96rdcDOeUxPosPJz6Hsut8ITSebQ6F94nN+ZhZZTt1vFabAv1LNonezq39nfc09oiIQFESlTqunadzN1UllS+9245WEtTGFt84vSFijJmPwZlUNO+Lt3zNgCIvKrVQkjRSrmg1v4ibWG7W7cm8hkNT3juO1cggtOzhgCivDGsfNhEgKDVV2AMy3j31R2b5lG1IADA2NMZtm56CXg4DQwDKhMtF/fY02NlmZMhI5oHmNIXyUVfQNHjftiXrYpKzcqWagMo3fhRbUJceLpYWbcqhFP1qWJxTZ7fmMsw+aDog1DkgrWBikjaFt893wWtUhNLcd+U+sHTbTsrh2cjPYCWTfWv9j31mRtrlbG6o1D/ANOpbp5WEV/UER8oVMTqt8mynqXIXtuONWTH6CwuZlg969DV774nh2vZb31iEnvvvyZqz5PvPQcwfqRWf2Kh4YMUZz05ZWg/18WjbXiKNDt266vCvlgshZ0nz/RJUr2GF3JOEae2PzpvVjGY4jZSugWtW5/36rW6JFUm27nQOWEfvI2JY/CianlWGzsGRb16VinPWdErY50CSRZ2vx2dxY7gLLKxUlNvXlDEtsmHqKyvsun8WNShy344ZJXtS+cJ1TKb0t77r8Fzd70MADi33I4Un9oH4ia8E17bWjmQB/Mt4d40zaX8MNbee3MUCdwLAR8iKZX1AxX7OqhfQHGSeRB57zkAEVsMoIbZAGKsiIK9irD2cd32FPfuKXmL2TMZ8t161jTOuVXoaj5ZoO4BrG1ZJaPFaGhb3kFX809x+kFi+/bbpoiu11QssW0pbceRrFFwNtRQQ0PRjuAs8q16jEQANfivgmaicua3NWHqKaCmQ71HHYIFUAEFRwEAIzObAGKFJGVTL4Bvb7EbYRxSFNzpUe3+HmekbSvop18bhexfjdeOn7qBSQhQSepV8k74oFsQfIQ1VXMuLUdUjK1epwWL9Ss7KE4Gr/WTzKtn4vo5NkZMS/UBkFNbuNt0O8ORFVXaZR8VMwPEpvF2NwbDWQ7sQsLq7RidRQpnoXBv/rb6CCrE+DFabITFGQCxzKw4iUFwb3qcqq4iBfdO6Vu8YDiekpI4CL3XDw9h6weqzUzh3uy3nUeryPXqBqqPWIPfAHWsAQFdns1fcQPeHHlu4h5+pJbMxyGrS9G+1UQICcCjZW0AnxTOIoVBse8mpVOwc6j9DyLVAF2X58HMOi8EZ7EjOIt+plN1zebvuo9HlXWMz8fyd51beOZDewAAnUfOyrNVPSnF2us/e9rkJamemT/SDfqU5YSLOrkQGzuDruCRwmp/p5aRjH+nXNSVy6GZuXXkWBQPsrfYLUyV8kGrmdOr17NE1HKYcOGV1zRLl62LY6i5XLPPYuLc7EzVNqveLQdDBrVBZJ3b2DdF+bJfes62zDi0v+rmnYqh0a8fKeqXpyW0T2WoU45w75SuyY7x7N+4tm9/lHY8Z0FKIeqUvBSEqft212fgXpI1iQIVjqDdXQ2bjWIs+lkI2B/PSsKF7yUx8hRiXlg9PXnsJpfyDVFxyloY+s2/7Qfn0PbXWloi5aJjcfAsG57VQ+tPKQyVPM7CziHLKXai3/eW4mZsHXrdjssj6xOjbXnzZa9by4p3T/u1nbB6jYKzoYYaGop2BGeRykimcjV1D4onABD8OTQQr57e1t/DsmfkKGy9QB1Yo3Wyr898aE/kpq4ntQ0PP4gzAnz7u+I/+qUCGIQ3ABDpVewp1C8VQD+MgNLSPW+rxbO4kFQAioFQfZI3Zynql0qQ/QPKVADSDy3Ld1BL7yDj1usaVYtkRamUCKP9BipukHoxbcMTa9h2LZ5FidfR9/ON7zyEv/rLF64cnUW/eBbKtgWvSEGpAUVMCiCPNhkGyanu+5sQA9dAcApA2pFsY66AgAcbfPBWPVtTGqZiH7AuC5TSGBMrh2cj7EIYayKeRaoNjjf0qcxtwlgOim/w4ll49YWAPJ2pmtv4zMJWvCmiDl0neWOwqEwdd8o6E4X2S+kPzMLU/th4FtoXHUerz5zbOBFerIkIBNdHjOE4UvEsWEZjaJB6i1XMCq8PV3w8i5SCc005C7oqO5wFLSHWBMZFEUxgR+sRlskRUH7t4GB4lnoLQq8nls6GyVZ9Q+eRs3jurpcx9nSx6bDs8qExbN30EvaWdWrAV4vbL5R+1W/ddBRWrUFzeMqxDRtQ1rMCdW+7NsTkaJUKVBLnS+HnN35Y/A7k47XPkookNsVAuKnw5NZ55MIJXF+34io0dgakDm7knUdK/YNnYZFn9Ho/hGxI93cgD/3wyOuvjhtAUCSfuO3aEJnN6q7YrqfTsJ6xQPHdLJdJhrpSLxMGWd8Qtq3vgve7t12L14uy+4qM7n0hkbLI/qciZSn60cZAmFmYCi+C8Rf0Wa0PqF4AXyTbpi/JvofXQtkZTGEZ12F9nqJetVEwOhdQbFi05dt4FlZs0lOap1zVRsxJrBzIg4hEVOf08bwy98opqQrXGaknMheaRVRk3UJURypSlkW9Bg5RuL7WYrcWfUu5E2A29Gt6f6dwnIu75FK7W4+UxXcec6lF/XYhc5zaX0s2RaYqy/V6yDeT6PegSFnafipS1vTxLHz3Fllq+3XVcBY9VGYrQlppLlO53IoLvcWuk2ekWmgax2F9fjTSdfDDKbxO696S+7qxyTEsssOz2Pfwmu91KpxA0dYUJpYq2X0GU7WNTi0bvcVu8BRMep2WHAHrL+pC+N+KPLxHmlg6G1lllGyIf3Iw8/c+HkHvQ3ljntQ+6ztTgBPbmD56KnCROh/qdcp3xJSNXNgeVwHUId4AIr0N9Q0ejJ7t0OvUG2vkdUrcx2HJ02vmDEBwAKvBvaWvntepkhfQh8Q5JrfqAdM2O9vzOm2sIQ011NBQtCM4i34UZDn5bVla6/xky3jhzJYMotHS3vuvQbt7siYGADH7bdugh+pzd72M3Y/GWb94mlkcRHFS+kjG4rSvQtoxCCvr17Zt/UochyIerdWBMvH0cYSyK4dng/JX6wo4AA33d8TBFxw5hhnUT+Tpo6eiZFArxhtSOUqKNxaUFeJ6Cifa7q7W0JMnbrsWN374yVrfGHrO4nIo+lgcS6EviHVJKVyHdQUnUlj1BfYZANF8qiVjfX42tB/aFMud9kPD6amObHKxG73/NoCzk0MZQoo+DV3yMtCwqQCWD41h1/xslHdTUwFomHayqvxAKLu2jhyLgsqqTEviRzqzsBUmfLkMtsvybGv3ozlevPUMNp6+LhJzFGyjC9DGimB5CyBjfs/d8zfjxVvPAEBow9bvkeoErBJQY5EW5Lv6c471fy4wteJQ8cZgLHaxTx/PaiJirf5S8auKZkbaJtl3pAsdKCxeniu4hlks/p8NGd88L9Dp47ORDo397C12w5xNPth13RKKtgZHx/VMutNHTwX9k76/lQM5Jh+s16GQAUUn857qhFovDexSVXb4ojufBuEYLJoSAFDmoginRHmf1hXFXUzos2ZBbt30EpZxXeSfsvH0ddi66SVsLgiCs9S9pOJtpkj70bvlYKifbW8uTEX1A741pFCs1v0t2t3KzZ7K0JUD58LHD1TWEG+e+f/GXBY+xjCnh2dr80gYPq0L00djtKa1hqA8mYuxHATEb4bkmVw5dqsb8GJkFmke6hgPjlMxOyzHsuOnR6Jnirqq/tMJLIKWO1yJXtPDZPz0CCaWtsoNoBO1qe1WbRe5f5WL6MkYSSNnh8dZ7QhQ1nbh3kp6mtr4A3ofqDs2ATEUWbNyKyCmH1BLnXosXJvtMB7G2NPVZkJzLoBog1FFmcK1BzmoFabb6yIlHdtQJSv7RIuMhbjbjZZlbYRxK56FCE/GX8T6tViyimK+L431oeVsPZ6zFpAwQRqnsVTeEasE13nz4Pz6bXobfwqYNggOnoK32w3HOwQ9V4WaLw+wLVBWo+BsqKGGhqJw5DF0AAAgAElEQVQdLYYM4wjDTNoWbBWZ00RH4HlPcsden58N7S0FkA1qyj1ATjSRrVU/Qh1Cu7uKsacLUWfrppcws1CZVEkVEKou01ozp3dSsw2tH4g5Fo8KNrbqR0p8CzgJMZ3yVFaua2MuC4F32a8Qq6O7GtVtociKxejdclA4ryrSl4cABcy7hq9vYAxO5UoonnjpEOzzlSgRE/ELts7otEf927PfMv9njhnbVzt/g8i+c+8b2OxMYeSFVwhnkWVZF8BfAXgZQC/P8+/Lsmw3gN8E0AHQBfCjeZ7/xfnUn8Loe+KIsmcp2ZX3Us8q+8gPeUVkZaDyL/FEAX0OQHD75qKdWbimFBdikYPPRR+5aNQtJoELjWIB29D6gXob2k+rcFOXbZXr+Yz92DfmsggePX30VKHYFGTnZmcqigNhx6v9ISn2xF73xI/U9xCBkbqroR+K3yD2wFqgqJNi2Xa3Lia0u6uhvmn9TvUblD5636MnxqgY7MUy1Tlgm3YOFZ9js9x7eqZh6WJwFu/M83xFfn8EwFfzPP/nWZZ9pPx9z/lUbLXXKddflav1Wb1Psh+dyq7eRqIKP6Aep7IOr44/Jt23lw+NYezpgsNYPzkeRbC2G5yVT7UN6xPANrR+AFg/OR61DcTBb5TUaqH1cl4DyrUc266TubsRWcuN+vfo+0kpde14Ky4sMc+1GmJif6wFinWof0xqHCmOi/VF36kzFs20pmhYj7NQK5Buut44+zmi8ZlIv+aB1V5wH3fpUugs3g3g18u/fx3Aj1xIZXqK87c9SaipXj40huVDY2Gh6v12ia8vUG2rAU6rCiH78fP3zMJW+AcUHMNKqeHn81QYWiK0mv4rMwtb2P3oOF689Uyokxr2lcOzePb2uQAJ9haZt8A0UhjrZxsbc1mon7R8aKzW12dvn+sbOn5mYSuCIE8fPVU61Z2qecFqOZ0jJY8tnnzwiQJiLrDsmYUtTB895Yog6h0LiHJwf2Wp4GJaOTwbnLNaR46Fjbq1v1Nj8QNqthybbYcUOXsJ8dtjvRY6vnxozD30qKQPfS2veWOPcEXld+zNcz/xZbucxQVZQ7Is+3MAf4FC8P2VPM8/n2XZWp7nk+X9DMBf8HeKLtQakmJR9b7929r3KdemNNmk4B2aOBUtrJrAKgCRJWRmYStYSV73sbUa0ExP9MKh68miP6XJz0YLUzg0F4INeAPE7vJeSgTl0FpHjkVQaLvAPeuP6gD0Xdj3Z61PS/e8zQ2LSNd0FdM8YJ62b7+TVOoHz4UbqMLle1ydhhEk1+pZTmw/NOCQnRfWZclyx9aioW3b+pbueVsE3af7vVJrfwdfW/siNlaef0Vc1N+e5/lSlmV/E8BXsix7Rm/meZ5nWebuRlmW3Q3gbgBo47oL7EZDDTV0qemi4SyyLPs4gHUA7wfwjjzPT2VZNgvg9/M8/+/6PZviLIAY0eb91ms2F4h9xiap4T2y0F7wGpvhnM9q0p9U+Dptk3WSCuhulSpx96PjUdBhknciWhnaCwDMNoAiKvnuR8dDGQYLsrk8LLeUQoQqJ2ZRkKnkTilFpsUMKOfE+lwX7xKLYbkxm9gHiDkAkk22o+OyCl4Np5gCVaXmiH2x36ANNDwMzkLvax8szsLDmwTRzIDRthOw97w3iyzLdgEYyfP8r8q/vwLgfwfw/QBWRcG5O8/zn+1XV7/NYhjyXlS/wKeeyOKxnBo5iWU8kccTb1RcseAqZflJL956BueW2xFIilHHAdQ041HezbINWz9QKQhVtNCI4doHD3AWIQPNxqeLmn21gYi9WJdADJTiuDQIkBcVK3VA6IbhZSfXtuzf1pxp5xgwQWucqN62Xs9qVzO5O21qOZvh3rue+q1j1u/bixb2SkXKmgHw24VaAi0AX8jz/D9lWbYA4LeyLLsTwLMAfvQC2hiKsyBFL8TRFKdemF00pCL3aGzH11O1qKuec8RS9TGPBnOWfuTLh8ZwbrmNkZlNbMxVItl4ueHTxNePaApVcxmzlzF3CFDY7xX7QKvC8qExNzJXivgBcwwbc2PBZTpsTGJhGYbU1JtyF1dKzbdH7JviYwbNaeogsWWGRyqkv0HtJ4Ck67nXr2jD20ZftkvnvVnkeb4I4Cbn+ioK7uKikJqa+DvSBMsurslX1NFJAS/qJxCoMxVxAlW78QZC35HeYjdgDHqL3bDI6smL6hwEaWMuC2CrXSdz7DqZYWPuOsy/6zkAwNoDN2DlQGGeXD40h30PV8+29ncCCIyUaqMQi0ajZwEE5zv2d/5dzwEPt+KP2IhourlNyHVS8K3g7xKjMn20zt2FqGfldcbSePHWM9h7/zVhPBbXwGuMHmU5qcCSG+7vubsmMbF00I3pYM2rQMkxlNc1rQMMt8AUjWEBl745nqlTDxnAj961MZfhzJ5zmD5a4TxaRwQvYax8PcRijppwPfHPGg1GRoffXhq4d0MNNTQU7WhHMmB7Ck5L/cxSSnVgVaUHsKYqoO6t6eUN6cduejoLAJGCc/KO57H2wA3hnsb8tKepJ1YpWR2AmnW1LzZCudW3KNmIWlYnon3zQE12fmz8Tw0hQJ2LVRgqtgOIzbte37wsbEraV40mTkWwhgtQMJ+n37DfiCfKpEy9rN9z3LN1WUVvP9HJU5pecdG9+1FNMeboKjwlUZRQxrkffXiiJaZ8nZJTvWCyFENSH4j1WrXyPgFd46ezCiz1wA2YvON59D45E+kTNMCN9lG17PrBcLHZsPR8hgvizJ5zIbCLDRy8cng2svpQPNLF/eKtL2P3o+MhNib72s/93m4gqq+o0jfU+x1Ey85UbdOyc8trFHM0HoXqoLRsJWZW9cYBoSsxk/EhUoCo1Fg3S3EWiMUI3YRWRFGsYk0Uwg+xrkfD6FmyAsd2xZAdzVmcLyjLuiJbUFbqJBgmrsQgjoHX+XGqnGpPc+t0BtRPxNZHl7H02N7Il8KevmrGTAHUrPu+fpS8z49OTylmXhs/PRIpHVPu7J4VyrpL6zjZPlNC2n4+e/tcIcNLbhfdBDlPnE/Pec4CzQCEHCAWKMUNVLmIKFWDmS+bbS1lZrWgLH03KfJAWanwC3ZtaJ4Rklr2SK8kKGtHEC0ACve19wEEpZf6bKj1wFV+AhGC0370KfFoZqFaSOoDohsHrS2kdncVy4cqtOfyoTHgsb1B+Ujy0jNSYWrNm54IED1TjoXPW4ex8dMj2LrpJew6Ph4tSpSLatd83B5NoHrN24AtJmRkZjMoRDuPfDfMcbH4RzB9NOYwyFGoqKS5Ze2mG35L9CkqeHW8zE+yPj8aInMBdbRqaLdMmtwO5apntF4PAZsSpcMmI+UYPCe6TwWnBDxim+SE9P1Y6i12MTJxBQa/OfyaO10dgz3Jh5V/U+Age98DBWlZTUBMsqcp4IOS+vUFiE+51AlB6t1XfA69T87UThhvPJZ0PKlgO3aeFA7tcREp+7+HCSEQDEDgFFSMsZnPPCxLCkCmICrrFJjifDjnKu+T0/Ay0bFvCt1XzkLb0Lo4tx7uoh8GQknxIDYhNseveiJmLbOcObkpfX47oKzGGtJQQw0NRTtCDMnGRoudtvztnb6bemoZ7TBZRJ4wk4vdKG+IxV9MPvhEqE812hV+odrBVbGnsij7tV6e6uvzxf+aqT0VSyKcfsrFODJnZPX4ZDkfH13GxCdnXCBZPy5H51TZ4Qj9+NFl9D45E8YAmOCvx2N0KgFpQAwistxRkQXtu+LF+12/7yVuQVl+tlsogBGNK5yoDjelQXj53ugyD6B4z/s7Ico4gFA/n6f4yKRSVhXI+tTZMIg1h+aishZybkWMmvLR5CjxHOdorUvlM4mcDYEicviht4Uym53t5Q3ZMWJISsE5bPAbYDA6bljlpJKy7YDvq5H67ZkRPTZyGEWV+qxwUXvQXk9s0rZUn6CkCkv1vlTy/DCsGKLPt8xCUvOrVf6yD5wPbccjLzanbtS2n6zXiqK2LfXvUbLzp0pQfX/0OdGFmvLz8Hw1VKTygt/YesNz8p2lTLp8Fzp3r4hvyMWkVBZ1wF+M1j3bW+iedUDL6ofl6UM8K0s/Wd6WL/J6jEcLzOsj4OsLtJ+2j1o/4C8cS1Y3A8SbMU8+ZpVnv1JOatYHQz9ozw/EvgegsoBwLnRRe4GQrYzu4Re87+GZD+1J5w3pM/fe3LC/YWxmY01Z00LekMTzKTyR97y2qS7zJFUyq3memCFt48jZL2LruavAGtLkDWnyhjR5Q3ZO3pBGwdlQQw0NRTuas9gueUAse7+mt2iSDIW/myRDTZKhfrQjdBZNkqEmyVCTZKjql+2rHZ/dcLxD8FIkGdrRnIXdIDwFWpM3BKGNJm8Iot9arskbcpnzhlxq2o7pVHfcFDvKe6lnm7whxaJRVp3P2I+9yRvS5A3ZUWS11x5noR+ix1nYCbYfnbKj3kaiMjzQ5A3h2Jq8IQj1Rd+pM5Ymb0hDDTX0qqIdv1koy8/flu2kprpJMtQkGQLEgU2sFU2SoTpdVWIIgNrLaB05FrF6nNR2dzWwycTM632gYsEVOARUyrWUKKKJWvY9fLJmNg31HB+sVGZdY09fB2ArXCe7rVBjL8bksPWzja2bXgKW2zUIM/vKTaPziK8EjeReI7drMBmFSVPX0c8c7CorqafZHwfQsXoJsv4zC1M1MTAVHJiBYvRbSC0W3WBYtp85PlK4D1iYHpTc9hOo+4ooWatf75aDQb/Ca57y90KD+e78zSIBg/Vktch/wpHn9L7VaVBhp2U0xkNBo8GstlGe/tOINxO1XJA8q8TKgWvDCx2ZmcSu49RhjNae83QXXhs1R6MD1wJl1PD1+QoaXsTRKPKGUB/Tb6F5RAuDzg3r8Kw1w5DCuIeJ7m31VP2IVgu1Ogwar+o3Uu2kdAYpshGtvH4Cdf2C1y8SMSuXmnb8ZmEnz+7wnqbdu08Kykk5JW2CIdZBuLeKA8uHxrCv2wknhN7bmBurLRTvI1s+NIbx05Xj0+5HudCr9snFeBGu7OZh2yDbPH6a3q/jARo+/bE1dB6ZKhf2KvYV04qVw7OuZWLFpG5UIvinKOsrJ71APTo/KgaGiOGYCpzhyntvBkquT+eW5lXdnCPvYYP1AHx/C+V+KDJww6qie89VSmjHCtMTJalnaVAHMIo/0+ivoPT8drSPQGEF47oIeB8gTjVRPrc2Pxpg8lrvudHht4Adr7NoqKGGdgbtCARnyuvUuhdbiDRQnfYaPYkngI0YrfI/21KvTgXoeKCjlFs0lZQWDel5GqbQn4MiZWmMCY5VA+iyjZS4Ek7E+3pYemxvqEejftu4jfPveg69T864GAeNS5kaL9+D9p3tBjfrjy4X/7+/OuGUU9QoVjQJ773/mqGRmtoPLW+jTwGFyVnrtqd575aDAY3LcuQwrBfztz4xib33XxP1J6X38LBEOh7OIZNps15GxNKyQOGR/LqPrbmJnxe/8MbwfLu7euUhOPOts5HikqzVJFAhJI8ci36ra+6+bnHNuuWyjHefL2Tfw8VHUbyM+CO1sSE0yI7Nddp5JA5221vsYnKxi4mlg+4CXj40hvkjxd/d265FBwdr1higEj8sJJz1F9fSbQBFGLvOI6U37WNjmH/Xc1hcngZwXViAKydnw4dO0eRZ7MUM4vEXVG3mAWnpxM9kHEsvEBBQLoJy49qHCqSlliWKGTMLW4IbORvKKv5gLaFb4OamIofqaAL25OR4qNuOt40C+7D7UR4ecTsWZFaED6jrFgbpcSiOqaVO69/96CzW56trVNoq2Kxoe82N6G77NXL2Kkoy5JlOlRRZRxOllRdJ/LDti1qfHw0gowJeXUymVbLpSVpkEYsVlza3htbPf/x9Zs+5IFuPnx4J5l+Ogf2w40+RrZ9t7DqZY/z0SHR/cXka+2dWcGbPOZxbbuPccrtv3xX0poA0mmvJKYzMbIb5bXdXw1hS9SrpBjOxdBbjp0dq/fb6pCbSfspO24+NuSw5r6pv0s15GEoF64k30LSr+sZc1ve+9ovEsXtuCfx+tmNRS9GO4CxS1NrfCUqmnvkdwXnLWBQ2kjFZP7L4u05W2vvAth05hmlUJ5qKBlwYNUey8h5QKrpKVlQhymQn291VdG+rwulTiamxHNQ0xzFou170JmVX2YbWzzZYf9ggD88CuA4n9lyPG9/wAk588/pybvIwnyxr86QqsU8VBmEU55ZztLunI9OqcmreBsF2iajkvDLyeZHW0VqlJBpUrWd10nbUdGotQGr90JCBbIewdyUrNm7MZZhGXRnM8Q3yLvasWjoOnUf23XIWLKNjABDloSVtR8G5ozeLWsQlZ3fnQgXEDVtwFqn74aWV92xcTF5TkxQ/BF3IhDN7CVyA4gVXuoyYU9HcGIQ0s4/klPY9fLJm0rVzUbVR1z+tHMixMTcWRIuNuSxgL058s9gwAKD3yAyAmOuyoeO8AD1VOoMRjJ/OavoGG2CGxI2YqQ/U8W7l8CxGZs6U5a4JoojWy7nTxdpPDPFMi3bhkqvhdWu5aHVXa6KuJYXTDwLTeaQ+K944NDEUsUL8VornY11fFAXd6ddVJYY01FBDO4N2hDWkX65Tm4siFRRW41lorEFl1xlTwKJCrQUEqMQSG+OQlgtP856KDapiDPumylMFddks6tPHs1o8C5Iqy+zYgXqwX6CyWlDfoFYJtUhoGzZbOn/r9eVDY2UKxNORfO7FneRc6alntf2Wk/Icu1J5Xz28iFonLHnKRhXHbBxULQPU42R4ddpvwX6H+s44j947tWTBirY9a32zIR+eGv361ROw10awtuni1IxnJ9++IN1E7AZhIcWD0g960ZxTqQBs1Grr5ep9rN5CABAie7Mekg10ou1q23bhk9rdVfTu64XcqiRuKGoapkys5lt1cdfx9Ba7OPGZt0apAPi8mrU9qxOA0G6RCqBghLlx6nvUZz2Tt2cRIdkUjbqhAkXaP7ZjY4Xw27GpADQqvBeIWAFdHjjLG1fw/pVDxksFQDcBe3jRiqdz/YdPfW5o0+mO3yw8m/crGd3btg0MF92bH7nKh14fgf7Rvb18Ee1ukYNUF69Xv1JqTu2JzdyqnBt+pJ7Zz5sv7114pyoQb3zaN1uHDQsAVO/AbgDe4lv8whux/8f/uDYnduHogiJ+AagW9rc+MYnXfWwttBPaLDN9AbHuReecOBE+rxYmO19eWD99nvdtP7W/Ni0CNxBb/5FzX8DW4tKVg7NIkdVW8/crEd1b21ZAz6Do3uFUPy7KRQOMsQrXfmP3Fsn8kW5IvAMgaiMVDIZUwNXjcem9kFsVwBL2RgrOYUjrt2PR5ECt/Z0CY3GoKJNymFMLQIju/eATmFmom9Pb3dWKo5Q6xspYpXYTsXFW1+cLB8OZhS2XrR97egwrhwush0Ywb3dXsXVXkWapd2+3inIum1hh5boOwFroKyOIeWS55627JjH29HVYOTwe8DXEyNhxrRyexa6TRXT59sNroX1yQBrde+TM8GrLRsHZUEMNDUU7mrPYLinb5u3YXmDTVHRv0rDRvc/sOVfTIQCzWDkQZ0rXNHgaFi/oW8p6rauxQqapJwEI967a8NLsaT3zH+3hWfhwb6A4VZfK+/Pveg69hRmntko0UFFq5fBslBme72FjLgteukW7xbMzC1sB7o2HWzXRA4gBbzxJJ5YqqLYqoG2KS5LHKfRuOVgzkW7d9BImlnzxs3XkGGZQwb21HFBxL71bDqLVXcWLt57BxFKlAJ0GojCErBeow73DNSk/9vR1QQHM6OxjT4+Fsqq3ePHWM3jdx9awMTcHcjLEihT9Kuepu4rWxqssunc/ra8nV9sgrSpTDxPqjc8p2f6xrn5KOG4AmvHaKkGf+VABttKYoKRazk+pn/fVEgTEwCML9rIWCKDQKxC4Fdp14nZ4MUFUUWnfkW1LFaiEjfP6jW94IfiokKzeSMlaoNiOWru0DqvToq+R7Xs/XyX73vSaKjyt0lkte0Bl7bPKUNsen08llbJ+PgBqmdUB4GtrX8TGylWQkWxYYoCWlFxtg6kC8a5O+VZ9RkheVC6vbosWRfmxKrqOC4kwbq8N1bYXH3xenKgPV320AKcQOFbqB/zYjVX/pwCM1syDdozPlMCtpcf2Bu0+F7Nq5j10Y2rOOA69d+Mbeug9MhM2DV4fPz2HE7ger+/GzngMvKORrLjA1ZIBVIpaDTLc7hYxMzwulIuMYDDWZ61IrI/6LqDQe7BOrZd9s4da4DIl9mbYGITLYN4RO68pSBX1MRbVrNRb7GJkYnhmYUdvFva09jTdmgogbACIIwRpuH5vMwiLvDNVO+Hty7CngcfeAn6mcvWCnVnYcgO8WO31xtwcdh0fx2anwAgUi8kP9W69bD0TqTWvKjSbuTztprtUKj1pJZk+XgUOZj2TDz6BtffejJYokanA8yw6NgrY8mNjmMFWwJVwUc+/6zmc+Ob1bh3cHHUeObcaZJnKRBtvxML5rWlSLRwowwOqCZp122/EIl6nj55yrR5wsECM+rZeKiSBSrSx3JlnPSR530etbGcKI9/4eu3ZFO3ozQIYDPnWsHoWqKX39VmXWyjhvJZV9thGW0dgB404oNcBYKJkMWl9mZCyHoyYWm390KaPz9ZePNvQ+nldc6bouCwMe2LpLKaP+85Vu07mYcMAgBPfvL7MkKV5UW8O86JmO12UUZ3zcbCdXSfzwP7Pv+u5oDfBY3uBPeeiOih2eTomWm/0RGU75CQ4N3Zj17nTDai32AUOz0bvn5Yvcm+sN9Y1xX3zRAubV5VtTCydrXEbrJd/b3amahG1dAPxuD37HprgNw011NBFpx3PWQxDg9yHvfueOBIFpwXgOR95sUCtqAP4odx4j5yM5oUgqUKKcG/FLnh6gZZpO5weJqRgdU/l9BjZaU8e4jmmjyMoOm98wwtYO34D1udHMV4ihTfm0sFoUzoLvd69bU/Apqx883pMy8m+66QTdkA4A3I3gdsxOovpo+qAVYpNSAP6WkeOYeOetwXuonfLwSiEIIn1xaJXxR1ovfpOWGaQzgLCIXp5Q/rpLLw595wdR0avkoxkl5KsaFL7X7JxWTdfqzdRUScSDRwLB30ybOxHLmA1rwUxSOJjqo7AikJaP1A3vwKIAEieElBBcCxLqDbh1mvHb8DkHc+j98kZvHhrYUrc/ei4C8jS8SuxraqNClK+MZcFczAtJFZW54YBAK8vgxuxzOSDTwQRL/aArTZjKg89wFlrfyeyJEw+eKzWBuds+dAYJkqdAu/3s85ZXVqkFIeKulO1DYb3rYu6jlOTXHkUzSMAvDC8zmJHm06B4RzJgHgSLVKQpLBhfd5TnnmLHajj8a3Si/Bb71lVfOmiZp9ptgPiuAYK1fXIyrPqmMZNoAr5/13XbJpKO5AysU4snU36qZBevPUMxp6+zjXZ2bmhvoKWFpW9OefWYY8KWcV6eKZq7bP+1meA6hCwPi6E1nt6E513fYZlWK8X4lHL6dgA9G0rUnxq5vmStD3WwbANtuyTE09cWb4h/TaL8yGNaZm6bwEx6qUKxHZ0DUJjP3y7IQ3iLEg2R4n3MWuEb1VI0mnO4yyUNNmwfjTcqDbmsgCi4iaiHI9+uLrhjJ8eCfEqCaqipWTfwyeDYxaxEh7ZueVC4obBqFtFGLhY/NJ3p0A7nRsvA7q+A8/JzpZncqYbP/xkhH/wxqF1DcNZ2G8o/C3fpkd6oDCnytp7bw5cYiqaOtux8/L4Cw8O7XXaKDgbaqihoeiq0lmE3fs87nsAJqDYjek0pPqEwEEklKvqIl+HgddFGzqdkWYWtgp9QAnGIqc0aeoHELVhTzkq4SjfUgdSnNSj4HlBEUojhWlfC1h1YR7dmCtO/PV5YOOxChq+9sANtZNXRTMlRhjz5qNwSS/ehsc6U4YntF31EuQq9GRVLpHOaDxlCeWOqDTFM8hx7xYGU471C639nVCfcpTY33HDJQBIJnLmt7lWOsyl3P8JO2ebrcVu0MeE9mGg7+UYWovdqF89AOcmrpKwesBwOothg99o3Iue04bCvSkGTAu72O6uFi+hBHBZUoix1ZADVVg+spKUW9fnZ4MYoDlIdz9agLEmls7W5XBHQ671A5WFg3gAjmvlQI4Xb30Z55bzYM04s+dcbZGpiLAmyYao2FOdy9oDhdKz9f5WWGTr86OhXktLEnOBbbj1EqYu863vLkR9R7l5UAQ176172x5MH602wJUyEBKfU+qhzsbzm9LNtNVdrSmRV0q/najO8ntJ6TaAShRZKeH9k4vdMGaG9AMQtU9riIpdNfHLjs3cH7nSFJw7MW8IgKTsa30UbNQolqV+wIuNANT1JXpfNzo9mbTvVpdi6/fomQ/tCboE+/FadCIjNml/lHRz5by86Uvfxu889HfD8wwoo6TxMagXIHI05XPDnCQAojpV92MDIw0iO4+M6uXpedSHBaj0GECcEoF6hBOfeWstf4qn7LZ9YXnWCxQbFWNvKKfGeBb2m2LbNm8IULz/Gz/8ZDFf+zvbyhuyIzaLnajgBMQWLh+FNVl6cF173bOMaL39INHz9z5exCCQWB2ptmomQIclZz+s5cSFI0uf210/WZByQvQX+ZH3FKfVQ793GADcsHp2Y+DGQdMpSYPJkCza1VoYrLLXJmRK9cnWr0pDa5GiYpFYGC8AT2qsep1krT86PqsgtZaRJcGE9AvBp/0Diu+nUXA21FBDF52uCp0F4OAsHE5hIM6iMxXFmiBwhtcou0c4C1TKUU1BANRPmOg0F5mZCkTVu0wsnS1OMiL7FAlKPICZA8stoJSf1dxGHcbyoUJ8UpOszewdn9BTNYTmc3e9jI4EwaWOghzFe955FL/5p5UyzqNI5Dwepx/QPmjiZcbjXDlQ4Cx2idIydaoGMFwJ8vKC8JIDYFmOt+B4qrwqQKXYtu76VDp6nJNyZyncjGJwAqI5tHIAACAASURBVPcr9xWMxm9Dx8H8JoAv6nj+JMPSjt8shiFCrtcTH03QpB+ejX+Xf090HB3JLQeLzNMPPhFN7oRVGJVtsx4bWFjZRoox0+WmoAuiV7ZHNpkQZXosepB1y5pq/UCscFX7O+dJsR0rB3JMPljf3BQRaBd8EfClqJfRvcdPjwTsxm/+6UH8o+89hj/Ca2pseDH+2GVeRYc4e3ysZxo/PVf+XzDGHgTeXtu66SXMLFRRwz2PX+2jimXjp+cwsRTjF3rwwwV4dSl57SoVGJMzmFm4plLYm+91UFveu9J7wwO8Yxqos8iy7NcA/AMA38nz/O+U13YD+E0AHQBdAD+a5/lfZFmWAfjXAH4QwEsA7sjz/PigTuz06N5WKaoQ3e1G9075I1jSvqsSTwMBeyCvfhp3HZeldnc1KD8tAtTqVMIJZVIicMOyAYTe9KVv4yufenvQARAQppBqPWk9sNrNn34qcCw2yji5OcruFvCmZDnUZ2+fCxG4zi23a3Xzb46PCk6+g9aRY5GlSBWh3My0T1ZpaoP2WoAW54B1W2uRF12cyFYbYZx6IQXLbSe69zCcxQMAPgfgN+TaRwB8Nc/zf55l2UfK3/cAuBXAa8t/bwHwy+X/fSnfikWDyCnHQG83xbefFBRBcqJOHz0Vytj7VVg6AJgKYoRuIvx4dXFFi1BcvwN7fHg2+ujV94HPsaxuOF4Wdfad/eRHysXc7q4GsYFt2BMlpW23IfnZV8KsWe/G7XPRHNrNpyUimkdcHF/51NvxAz/zjWAlidjsQ5XSVMMNsO9s9yufejtwoA6P1xSGfE+e63qKNOHyyoEqHMAgi4pyLhb1mool4dVrcSbKxfKbmFAl9aG5WuAgpeJ95ugtdkPSb0AcHrEnev/byUg2cLPI8/xrWZZ1zOV3A3hH+fevA/h9FJvFuwH8Rl6wK09mWTaZZdlsnud9c7dlY0XnrbypC0qdbqytmf9zYlI5OdT86slyzJTNsu3uanT6pU4tsvY2EpLllAiespvXoAUHVCy3RnzyZGILG+9HloVmBO1+UOjzoY25DL/z0N8NVpKF1X2lB2u1kfaTpStwGaOZZwFYdiEyOFDhTpS8Bd+PlGOoPFvzII5ZnUtcLj2/CtTS5z1HMrXcrBy4NjwbuNugD4vDBb4S8SxmZAM4DYBRXecBPC/lXiivNdRQQ1c4XbCCM8/zPMuybYM1siy7G8DdANDGdb7iBtWpGxSJ8BU6tWflmp7w/Vi41PNWKeXpBbx6VXl6ocRTambhuyE8HOdsO230U7AFRGNZ766Ts8myg+qwbW7MZVhY3QcAODT1LE7geoGcD6ai34PPNsau1KziaiWaNOU35rKAYuXz+r9Su7salKsBMFXeq77fSvmqkc75vMeppbhBVczz+9cIYBSbLWdFLpQRvlJjBbaXGPl8N4tlihdZls0C+E55fQnADVLu+vJajfI8/zyAzwMFKMsrY0PieTE41XTqKYmsdr+32C0gsg74alNkQ9XGK7vHdmzofn4Ikbu08VTULFoqsigsVz9yQDJ7l4o3xr4EEIFzNjtTUf2sw4pCXjg9Ha+Gc1MFHBM56fV2N2Zzlxw3d/VyZfCcE7ge73nnUTzxyJujRUKLkJJ6yHKj25gr43SWGcN1A6DlyG6gRNiq6LfZmYqAZVRaKkzeEvugoqT9nlhu66bNsr/X1cRGjmNiCbU5AGIglrodqJ6JMHC73EMgYlTiyURnqnZQbHamCt5/SBoKwVnqLP6jWEP+JYBVUXDuzvP8Z7Ms+yEAH0RhDXkLgH+T5/mbB9XfpC9s0hdq3zzEplWwNukLX/n0hcOYTh9CocycBrAM4BcA/A6A3wKwF8CzKEynL5am088B+PsoTKfvy/P8Dwd1Yif6hth4C2xLn9XnNe8FUEGMbRbxlFnYWkMsKdRak/cCiNroZ18HgN59vbAZ2CRDNtfE/LueC/k67Gajpr/UeO3GrP3XDQNAlMHdgzxzsQDA3vuv6Wtq9GJ92PIKg2f/GKPDOyT4bTDJEMtxc7KHj2ZtV3O/t6FbTthGa+McapZ5zgk3C/Vheu6ul/G6j63VfEN6i10sfuGN4fl2d3VbviHDWEPek7hVc+YorSA/NUzDDTXU0JVFO9qRrF9EKhL1DQrUslGqPKi4dcKyZbUPKbwCn9dyFqwF+OIGUHeAAqpTThP6jMxs1thX1key4C3b3xgRWefUkkmGPrRnqCRD2p5yhp7exOtf774eep+skgxpv9T7Veth/3XOyO2kkgzpGD3xhnXr2PRZHUvQg8j35tWp15UsiFD7oO9GHQW9789bF/welDP2nCBfdWH1vJBlnpLTLmygHqKNNBBfb8QGj3XUjQxA7cURdce/7cekohBRiso+283RpuzzEKvewkiR1ZlE2dJKb0u7KAFEKEcgjnNJsv2hvmTtgRsiZSYXv42V6emUbE4OzqHOq3Ux90Le2fgW+g1EwWNKRKciIr0NwBM9tZ1UCEYPzOUdhJ4XtTu/zkF4Vbmobyf4jZ1Qq+yyz2obnmLNLnbvmg22Oigeo5rxSGqhePHWMwCqRLtcmFYPwL+1Tvu3AtCs3sae+pSxvRORG0O4Vv621zVmKH+zTUt2rqiwnbzjeaw9cEPNauPNa8r6Y+eXOWO1r/0Uz+yfbpI2IjvLAPGJ7YEBtc+R1cnZWLxUial8pkB/jtt+e7ae3mIXT41+fWgX9R2/WQxD58NZpEgnNxWM1XvG+rCEts1JohyHjWcBoPaB8pqKEyl21ppq+0Uo90yonr1fY0zYjGRU7AIxF2E18Ryjkh2HeltO3vF8UMICBcZEI34HfIH4o5A4V55iOkLPyomc2mhJyvnpRt3PUqRz7nEmyh15AXu9g8vWpeOw8+xxjd6mc1EVnFcCeaYmBap491PacV2UNkzd+nyRUMZyMZpkiDiBcNqIeLAxl4X0eMo6KhaBH6nNPWpxEtGLL9vQ+tnGUrnpUIxhwJaNucJLdGSm4GTw6HhN1l45PIvOI8W1ic5USCk4fTLHyoFzaHdXMTJToBwYM3P6aPVeVIxJ6SsiXUpZVlMlqmk1LBgg4AuKd1b1l+90GjFZnY6GqqvVX9bJTW8SdR8O9jllTtZylV9GDO33Dh6N9al+UVZXApRRucrnQggCpJ0I1X2dZbYDymqC3zTUUEND0Y4XQ4YVMTxuQe+TPFHEUxT1U/5ZnYnWY8vpdUVDei7sWpYgGroZq+uzTXjD57R+IAacWTHH4hhYt82iTldrzaJulY9Axapb+T0FLKtlUZc5UZm99dFlnPjm9TWAG+fX6ies6ztQ6WkUmKQcgZdF3T5vORObu8PqrazVqh/GwguVOEipzb7buLF2fqyOxJY9/o1/NbTOYkeLIXah83cKcBNYTGMOjVhEFC/Iso+t0p+ALG0/qwHrUVJWE6iCu6i7ND92umcvl+7Zk46Siguy8C8YCXk2p48XL18jW7ENrZ9jsIF/lAg3Jk0fz2oL+8Y3vIATuL6Aape+DrtOVtBzkr6bWO6fqsn17KO2dWbPuVCf+lSc+Ob1uPENL6D3yEwdwdmNA/QAwMRScU19KNiOwp2DgrRbz6JOfZHOw8TS2Ugpyyzqdsxav/aLcPmaDgzx4cbv1SpgrTmVZVuLXXcTevb2OcxgKiiZN+bGMH/EMf1PXFt7NkU7grNogt/USfveBL9pgt+w7p0e/OayET0IAYRkMvytsSEZG2BauIlWdzWcbPxImdgG8tKWD80VTj6Ig9/w9I+xC7IIxclnojNVOiPNRqf6+nxpdiyVfrqQrQVlUPAbLqrx0wCQh/oBhDbs6W1ZVqCANDPYiw1+M356JKp366aX0H54LZS1m48X/EbHy8Xx0O8dLgPX8CMfCfO7dVfpBvZw/+A3D/3eYbznnUXFXzn+9ig1I+dMuQFrIeGitDEiZha20N1T8IR0buN7rzAkxbfVQ8FpAUDvkZkwB3pIkLO58Q09rB2/IXqHQD2XKblDVd5a0z7rn1iqwgMCxTdhFbkIfWzVuCP2i30fdGBZahScDTXU0FC0I8SQi6XgJPVTQFp2r5+Dj603tOnYq61MmUIDAnWgkYfTsOKWddiKooibNjzAl9cHT2Fo+57CCAA+CE3ZZEbhVtdyJevOruKNpyQl/cDPfCMSSSxew46booKnzLVJp9lvZdXJuqvIoXWpQ5eKpUqeuMA2CWYjWci4/T5S3zDnkO/FInftN7rZmcKxr/4L/GX+4pUPyhrWN6QfUq2m4DQaZNUh2I/fazelD/DKedpq2x6QTgxDd2kvZL32wRsP/+4H//X0C9ZHxNNxpLwoPcsEUPeS1XuedcK+M4ugfOZDe/Cedx4NehBP32HxIh6GhNgWq4u5+dNP4Y/+4WuisqqXsqRZ3jzPaI7XigXW4mL7b9+d6md43UNl0gvWWnXUu5rPP/XHv4SNleev/M0CGA7uDfgOVnYBD8wbIpSC2PJU9xannjApjofXrKOSl9+D9+yYvMS/tn72R82RQAz31g//2dvnaqnwdMzqm8GP9MRn3hpS4ekHbZV8nkJV+6ttsB8k7/0SZs4gwFbhp4o9oFL6WW5BUafaB2tmvvnTT+Ern3q7a6FIjclueJwX6+JuOUcdB8keBp77gnLgqmjW+vlbDQGNI9lFgHsrS2zr8Z6xnMYwHIjV+gPVR8KTIQU59zYhW79tA4j9ZuyYLGfg+TzYevRZz1IF+B6xis/w+mEXnRUXuPn9yHu+HqKGA37SZqAei4RjtX4YQAW1t2KTjZnC+qyDIBexFUVSpI5zapnTTUHny8N0kNSp0Ipn3kH5tbUvDs1Z7GhryLDkxe8cdN9e48tpy32FcQOx7KgbkOIrQr3OBpISpYDqY/bMhpGOIrFJDKp/ELFd+/FZPQRQBtvpdmoLeFLKA0W8yImls8UCAiII/iRia4pujsXYqjkApoyoNhWSMGnU8N956O9ifX4UWze9hDN7qrdCjmL50FhIPcBsbNNH61zmvodP4pkP7QnxKnedzPEDP/MNPPHTb47eL+tT3Qth5L3FbkgOlMJhsK5a4qpbDuLEbdfi9Z8tLxw5VisDxH5EIQoWEMIK2jZaR47VrFcjZ4dnFhprSEMNNTQUXRWcxaUgj53uR2onByqHoUhcMOUtQEeJJzn1DqoktUpH27Zer1leyqC4DGhrAwTreDcFc6I0fnoEm52pmnxPXca+bqeYg/JUs7EuSa3uaoRraZUn6Pr8aI2F10zhdq4ohlAkGXv6OjeKuYW/TyydLeeymgs6DxaetnEbM6jeA9+Lh21Zl/nlOC2tHJ7FpOifrCjx+m4shpDoHMjxbHamYjd90xf9Hi2H4/WrH12Vm8WFiCXKknq6gZ4pz7/tQiULaK/b+0AR3LSuQyg3k8Ozrvyrz/drI4KEAxEsHIhl+ZXDs8GrVkl9JorfVZ38OIlw1GcVLKVE8cqDwnsw6YklD3BVbFYTsiFSJCnMqhV6knI84d2hf0eOYdd8jL78gZ95Ck8svLmmR8Dh2QixOv+u57CEvaE+RfTazay32A0iWAW6Q0i6/cyH9lSKYlTiEAFXrW6s1Ax+M45+IoDPTPT51pFj4W8FNGbffgnD0lWh4LyUZJWl/coBdYyCUj8HN5KVbb0I0P2Up16f+pUJ1wdE4LIxNajIsyhUD1eiGxv7ZZV4a5Lx3dP4K9lAzIRrjz19Hc7sOYf3vPNowGGoNcbqXixcvLW/E8ygaj1hsOC9918Txrt0z9sAwEVJ2nnup7NQMyevU++j0bitVZBzE9owUbCs1dAL/NTav71IWVclZ3GxydqxPbLX7YuxdVlzVyAxdU0snRVIdnx9UP22Xs8JL+o/qk0gcCr7qzgZG3NV3pCNuSwo3CL8RnlSWlAYIfakIAaZ3BjkEtbnq1N1qXTAsr4a7W7lLEZlJhMRKTR8YXUfWg+3CpPsu57D2gMxBHt9fjaMYW1+FOOnM7S7q3jPO7+Nh0DgV44ze9pod09HqTNZnyemRqIDUNuIVUTwklhpDthwrXwnpJZwvh5uyMXfGGDiyGgTz6Khhhq6yNRsFgMo5b34StdX6B+KE8HLlHWh9W+X+vVhEPWDoF8o7TpZQMwXVvdhYXUfDk09G+4tLk+HbGapvvCUZ6rFfu2wvguZ881Sh1RwVNX4berDFG3HGWy7jmOWdoTOoslINjgjmb1+qTKSPXfXyyFY8MXMSKbE+5qIR+/ZOqy1SH09rI+LDWIEFC7yf/QPX1PDoTDDmLVEaBoH7b/CwK1C+pkP7QGAKFao0rc+MYn9MyshTKAVF9RnxEs4xZQMrfe3QtsnPvPW4LMS+i/9UX1N97ZrceOHn6yB3449/xn85X9dvjoQnNuJ7u3FrbBKNBv3QtvwMqApFp9lbRwHGyMjdZ31BW23QQNqdO+9918TwbX7+awoxHcQwo8bkPVVYBYrTzFnFyR/28C/dMDSeBhUDFoiNF77qrBszyHM6kIshJvjU2AYUEDcGVNDyXIVFnZv86TYDdPmYgF8hK32zTuk+BxQpSqgkxuQ9h0ipXyDrOOb3Yh7i6/C6N6XklJh+YZ57nwUnJ6Phff3+So4U6Sbo/WnUYcwAC5E2sKoSZ7IoeY9tmURnAp7txYHtqc+MAAixykG/F1cnsb+H/9jrL33Ztz78V/BPR//gFs/+0orhKZ5ZL3z9z4erCBn9pzDr/7w53HPxz9Q4/zYJy86u+eroWZpjks3Qm9T0bI2CBIV1Z43qo4dAB5/4cGrI6ze+dIgc+cw1o2Uf0XLcCup55SrUf+KNS6UxSq0X0vAOdRNR67o7705cEPTic2GH8aabCxeX/ttINNHTwW4cLgGBOhwNQ+jtTii6xIyTrOSe9yZQr3Z1szCVCm6iQUFiMyHSee80nLQ7q5iX7fY4Gj1GJvLwnP3fPwDuPfjvwIA+ODnfxIzC1u1nCbETaw9cAN2IT5IW/s7UX/uOV7U94vvf18ow01gXzc2o1siBL6NOEQfQK6oCjNIrAzHaLkWdanvLZaRzQOeo9po3PAJ28ii3ig4G2qooaFox4shl8tFPSV38qRMuajbtPap4DcahyHFNqo7umUnhwl+Q3HBCzGXEiWsSKTss1Wkqd6FSjpluVMiRKjDJFrieL14JJ6Lug1SY9lxJRsj9XN3/7sgQqSeVz0CdRY2LJ7GagUKxSL1DZ4ug4pQIjY9BKaXV9VzDdB5sUhcq5TWe+qS/63f/BdXh87iSgp+Y9vSF+al1wPicPL9FGKaJMgqBFmu3wfC8kA6MbJaiRTm7C14rdNz2R52rnRztRu2vjMtR7K6BpJnDdHUjKozsCIEv42le97mRq6iEhiIRT9VXAJ+usY3fenbAVUKIKRc0I0VqNzgrdi1fKhICsVNBogDFOu8trurkc4FKFCumlwbKL6/b/+fn8bWc1dJ8JsrhYaJmXEhcO9hc1B4fRqGBpk+7SawHZu9VV6qJcZyLh68W+sAYg5AOTQb9AaIN2gvb8jP3/fvg9JT55m/2V4q+hXd2TUXrRdPwz7/4q1ngsXrzJ5zbhJpa9Ui6SZs871qRC97oHzrE5PY/ej4la3gzMZG0Xu7n0jlSksFQG25p9mPcp0mFlud9az+B6oQbp4WvBYzc8Cis+2y7ijd4kVIBcCI4lR8jp8uRIiJpepE3ffwSaCMBaHvn5vl1k0vhSjcnUeqfkx0piIlqfaFxPo8T1RVet755btD3Ww7zG35zahpGyjuM+w+aWZhK0TEAoDdj46HfgQu6NFxLB/KogRIAEIMDADRe9iQMHkcx/r8bHiX7BcwixdvPYPpowxdWClJdz86Xpar6j33S8NvAY2Cs6GGGhqKdgRnkaImbwikn1XeEJ7eV0rekHPL7cBVkKaPFw5bw+YNObe8Z2DekGCW3t8JcTp0nFaZWXBAU7jzy3cD6JM3pHQX7wHYP7MCAOhhxlXckhPbP9OrOa2xXfaJWeba3dWQ0FhN3ax/+milE6nlDSkjfe1+tAJfTc6sAE7ekOmjpzB5Rw+9pZlQ7/O1EaSp0VkkyFOQAsPhMrTsIOQdn/WsO6p09WTlfvqRVFuD8mKm4m4qcIjkiSGeviEVD9KKC1bE1HZTwDQVxzbmshDPQrOBcUwK0+dYbfyJmz/9FJ746TdH7L5iXsJ4P7qMpcf2BjGiXzyLfnFcCcBSxaW1NAH++xsmnoWHxdENq0kF0KQCCH/bj8wiLZtUAE0qgKsmFcDlpgbuvXPg3jaeBRejIkY35rIQAAco41m8Px3PwsM40JdEExmtHMijDPPLh8ZCfd4GYq1anvI+hZ3guD306jDt9DsUtY3t+obsaJ3F+ZLqAFL3dcKA2CGNRN2HaqXn7+0OxfFovfpCKUtPQ160vERaMIoM67Fugn2YWKpvBKxLZfVpVB9iyyxmBWcBxcm2fGgOkzIGau8nluJTcNfJPJghK2/LagzMDq+WFEsaDZxlgUq/oNYF21eG9aMvB6Nwb8wVfZtZ+G4IXAMAr8dp7DqZY+mxvQHCzQWlm5CG0tMAOk888mZszI2FfnMOtD6ds3bQl5XvwdlMlLwwfDMLU66YqZaTfQ+fDNwfXdo7jxTvVyHipOpdlO8KAF74et++KTXWkIYaamgo2vFiSJORrMlIxjFZuHeTkazJSHbBNIzXqYd29Dw5lXSiU+X6BZpNKUs9olLPBpVV4NWg+r02UhnJrIKRZXu3HPSTDDlJhbmJhiRDZf9TIqH2g4pcuzkq4M6zFNECAiBsHC/eegbnlhNJhkpSJa49OKaPngrBYzgOJhnSQ8uD61vxlvV6ugXPwsH5Z+Jm1mVBiToPVr/BOm0b9j209m8vYO+O2CyaSFlNpCy9Z+toImU1kbICpTYLL3KVNUtRyWXzUOpC63cCKKzbg4Grcs5blHxe2Vqgss3zFCaloOzeB6KkCj9ritQ2UtyKLlDCj63PifUzePHWM5Hzkd1YPC5ET0Td2JTGT4+EMXzrE4VKlVG6UuIbFwuAYIHQxcZ51L7o8xTxFGfBOSRUmovZ4jeUs/mnX/kygAImTsuJio/kpn7+vn+PD37+JwHEZtGUZ6t9T+wj26dI2frocrDoWL8YoNjgb/70U1h44zVRcic1Cz/x028ObfzhU58bmrNoFJwNNdTQULQjOIudiLNoEJznh+D09BMW1XihCE721+vj/Luei1yzFcEZZf4q+2r71vrocsBlKIKT+iqKC/d+/Fdw55fvxo0ffrIW96OfspKkSmgANcV4ymVfvwOPMwkciMOp2jb3PXzyyvM63YnExZVKDZgqT/IAV/a6VybWjhc4B6tItIs2Bb7yrqe8XVOafQVl2aA60UKnx2ipiScVOA0/+A3bAEpxDgcrHAoX2KG52kZh+6wWpCUUG4XG4Jx8sFhkv/rDv4J7jksMzv2dyDNzYulsAeHGyWjTmSjFjPkjFfL0zi/fXcTgPP6BWt9UNGY/UyL2DIq6w6Zbigv0ytU5Yt2WdL4pRjJ0oYrdLOfF2xiGrsrNYhhrCKmJwVlRE4OzicHZjxoxZAAN2ngudX2eSbcfQnU7G6XSIJOvPuthPUgeWhPwWWag7t+S8nVQvwwiFpkywEbr0nq9U13LaOAallEl7aA6gCKADjcMVax7sHfWQ2vW5B3PY+mxvRGuxjOXa5/7KUXtpmqxPVZ8vWpMp9u1hqRCz6lcaE8nlfGUbfOcrKy9G6jwAfakBYodf/z0SHR6eSdkP7OkZ0akt2QVTGZkoB+BjjfFhlLk0ETD1rTs9VH/VrOy1mtBRnR2ItGq4I2Z754iwPy9j7sgMMA3ndoNgcS+aj9Ytwd6I8iM/dX7P3/fvwcA/LPXvKGGTSFuY2RmMzIVq+6GuhCWt/P9T7/yZbz/D34CY09fF9pOmYT1PdKaRX2NmoU3O1N4fPGz2FpcaqwhDTXU0MWjHcFZXKiLuuUKLMrNg4p7ikYPocnrpH4sumc5IQsNpHN8elgBq/32ArcMgqx7/VXOaVCE755RVvbLvNUPzKTtKSnuRRGgHgzai8LtBbb1OELbD6Di+tSN2yJIPU7K9se+i5/79jdDTE8SxQovO5zCs9muBeKplUoVzvP3Ph44EusZrNHHOTYdP59Z+sy9V0d07yuJrGWjnydq6j7QHwIO9Edppiwvw9AgBKYHyBqmD0DsN6K0nYC9qTb1uhe3YlAdNn5Hv7GxDo5FXeV186DSk6kIAdTSLDJ4DoBow/ZEUhusmOKnrVPr0L6S7IbVOnJsW74hA60hWZb9GoB/AOA7eZ7/nfLaxwG8H8D/Vxb73/I8/93y3s8BuBPAywD+lzzP//PANhIBe68kBOcJIjhNlGVFVyr304OP4EyZaoNcfWguieC0GAUNeXchCE67iDx4e0BwmkjX7e4qToRFU+lYGLawQnAWliDvQ293Vwu48huKEaUQnBOcW3mP2t/lQ2PB2kE9yq6TeXDu2j/TAz6J0L6H4KRu4p6PfyDUp9/YrpM5fvH97xuM4HzgBuw7mk4BwT4CiHRik3c8D5SWHtU/KQfxpi99GwtvvAbLZiMEpvCmL30bT/x0OU+3HASeii1V/WggZ5Fl2d8DsA7gN8xmsZ7n+f9hyn4vgIcAvBnAHID/B8Dr8jx/GX3oQjmLFKah330vqM0wEbG0vFdPv+f6nd6DytpNa1D9/frgkW6unkJz2OjeXpv8kBlzAUBN2ecpFIHqBE+F19eES4Pg3mphUCU44d67Hx2P/E08cUfzewAIymYlijifu/vfAWDU8DhvKTdJbZNtkdQHhH5GKs7pIXfiM28tnnc8aYF6mgPSM7/96YsH987z/GsAXhymMgDvBvDFPM+38jz/cwAnUGwcDTXU0BVOFwLK+mCWZT8B4A8B/Eye538BYB7Ak1LmhfLaFUdq/1cahFOwYoQ+zwji/L+fO7yy/IBz4jjAMq3fth0QnKkIYvs7NQWcKoqt0jDcU4XwkWPYdABINiwe72YmXQAAFnpJREFUAGzMXQeggiVDFMQqWhCEtmt+tnZaBhFI0KkBDOcMcWLpbISUJFEsC4rKklOIxNqyrDWZsj4tu3LgWux7+CTu3FNEDf/VH/48/uVnfzjqy7nlNkZmNkPkM3JtkQhVjm9kZhPTRwsTaLuLIOoGdwBgqIzr3dvqJuSRs95M+XS+m8UvA/hFFELoLwL4FID/aTsVZFl2N4C7AaCN6/zODWsNIeudsoZw4Ys1xGIc2qjjAbx+hOecMtpWYOkRs4Jt1tGZwlLQpaTh1l76Qmv9aJv2Ql1G+QcUiEyOw+qE2t3Vgt2fr8qm0hcGoJTOi6MIBaqFpEhEQqj3dTvRfNn5VTARqcpZOhq1AyAgY7UfhSt8fSEVLue1acfGXBbC9AMV9F7TF3IeCpoKnrXTx7OQqIgL+F9+9ocjnERxbwvANeCGGawaEqCZG+brup2A03jdx9aiwMWdR84Wa6Msu0n911evx/oDlcgyeV8PN37/kwVqVvRym1+PQwT0o/PaLPI8X+bfWZbdB+A/lj+XANwgRa8vr3l1fB7A54EClOUpOFXh1bvlYPg9MJ6FLCYrw3Nj4EKdQfWsLgjWafH1AXjUjctZfUGhsa78O/i8/s9FqWTnIASQsQ5moX6ENmz9SuyDnt6MxahzUwCyig96ojMVpRm0G5o11XqKUDtHOo9bd01is8wiZt+n1rlrfrbG8ew6WUdJpmj/zApa3fhTb3dX8dDvHcbrcbq22XgpEFnPZqeKR6E0MrMJAJg+WsTHsHP1/j/4Cdz3lt/AL97/PnhUzeVoqFsPqZGZzeL5zvvC/ZGZTbS7axEXxYPyW8uTGJOYouG+cIPT+zvYPf6S2x+PhjKdZlnWAfAfRcE5m+f5qfLvDwN4S57nP5Zl2d8G8AVUCs6vAnjthSg4X21h9WwEa6us7BcNy2402wmrZz1Obf12znqLRZoCHa9naaLlpp/Wn23whFVfEvv+SAxRRwWh1um1Zb1madbUAEUeFgIAnrvr5aCMTeEwUmZKEq1mtKZYfxLFSViPXPsdDPIgVgyNhZxbxOt24lkMYzp9CMA7AExnWfYCgF8A8I4sy96IQgzpAvgAAOR5/l+yLPstAH+KgjH4qUEbRUMNNXRlUAPKukjUgLLSpKkUlKzfyXaympEs9iblVGZNp0AxPxZ3kuJMrO5Hnb0Y3i7UW0azokn23HK7BqBSHIZyACpO2vikJIs4VfOt4k/sOBi0WOu/qKCsK4EuBc5Cr/era9AmYdv0XNf1b4Wbe2W9vlrxYJjxWmodOQbs7wRlHhDjKSBiBn975C00lcdJG3NZlN9Cg+dsOo5cFv9Bpef6/GwY52RpJVKLEMsXepIYqLbZmcLe+4HlQ8QmFPV7zna0UuhmRJd+uuIXdZzCs4/txb6jJ/HirQWWotBnxIrED37+J/G5u/8d7vzy3ZGo4IkZZ/aci5zOqBTmvFhRLSX2MZdt2PA6U9sCZV0Vm8Ugl28XgjzktX7xIIb5be9pfdupV6/3bjkYlFT96vfq67dp9Ba7IXYFqd1dDXEngkkW2+MuqtO/+oAnXLV31ab2idYebixcXHZBBCW4XNN+qgkUKMyn1kvWW2SbnSm0Frs1/dQMivom73g+BOIt5nkOm50p7H60ChYEbNXqZQCdD57+ycA5sI/aD9WrWJRuKON4qtqxk0JdR44BE7WqknRVbBaXki5mLIvt1GcXvZoUW91VF0cwTP2DYl20zEJN9ad15Jjfh76cRUGpFIxBie20OyH1qjizMZdhQhSBRVTrucihipsoFzj7MH30FM7sORctsMINvS72hA1a+sH6lh8bw/yRx0M5za5W/F+NjeICRYgPnv5J/Mk/+bf4/v/xztCHCBZu5gb7O9FmunJ4Fs/dNRlMsnS5r0Sbamxbd03iWUhk+lsOYuQbX8ew1OgsBtCwwWqG1Vl4YsUwNIxIYTmLYcQqrXOQeOO1nxKBLKX0AF5fUxyetbZ4HrkW6u15ifI3IeQKn/bigCr8W09leoumAt1oWwpbB2LvUAD46n/4VQDAD739R8J8KVfgpWQAKpFJYegp72bb1425DM//0qea6N4NNdTQxaUdz1kMg7OwcSOIF7AnLSM5W8iz9TQFqt3cxo+kEs5miLKejt511mfDxPGk8PAQKdg5x857POUUK+Epu6jgYhJgnkLEiHgYDs+xTE879t1mGuc1RtS28+hZXTwnLipZLZI2Fc3MGwND5WnfWL/9lmzfdB5TfSZ5CGBiUmxKRc/69H9/43fw2t+/A3vvv6YW44LPkZTbsONXt3agwplY0e/x3U9iY+X5V4815Goi9cyktYAKtotBlfwff2hnbtsT7itR2VahPqvf6/Oz4dr6/CzGT49Ei2djLsPIzJm+fQl/lxtqBbU2QWKkvIbv181YRQhdQK0jx4Db3hqsKCSNR8m61K8k3Ds8G1mFSAquU9GotoBLPYYHrtL5BYDX/v4d+LN3PIDX4g6MPV1aaVBtQjYUYW+xixUJOsSNku15mfciV4hGZ3HxyJ4y56ugtPUNMnHaE105KwC1E8Lrr1dvP52FPcGVYwFiWdyL7WHL85mk85qQpu8DYoWgrcOLsQkg5F9VBeiZPedC2j7NjGbT/mk/6HuisSAYw1K5No1i7lkjSB6HqFyMRVU+d9fL+LN3PBApPUneZnHiM291Y4yyn0A6d8nxb/yrKytSVr9cp3aRWAWUx8pxIXo+CXZBeAsEqIAw3j3LDitGYOmetwGo8jd40aBYh9Zn2WMCbVSk4jywT5GDmmIiOHeO0nAQ6Mkq0FSc8jYy+7zdSLzExrYufTf2b/v+eX8QXiU1Pi9uhJ3HfopiKw5oOED2jeU0zoYX+tETf7nZzSxsBaXn3/nX/3PgDKz46m3kTEJlEyavvffmIH7yme1sFo2Cs6GGGhqKdgRn0UTK8uvVE86erB55nNSwpG3Y56zSr9+p7r0DzXhO4unZr04dk6cQtikTrfKblKoXqCuVeS/FUaYcA5Wj4NjorEbXciXmPmFkq1TIRgD4k3/ybyOTqoaJLNzUv+uaZNV5rt1djaDtfAfbCdi7IxScFxqDc4l5Q8wCJutJd/T5e+vsb8gbUj7rtU2KlGllecq2XoYumzdElVraVrTA5V5b/tc4Ga0jXTdviK1fScfr5Q1p7e9grVw4W3cVMOWQN6Rs3/axd8vBKFZIq7uKpXve5tZPuDXncbrU2DObGGN6WIWfzg0XD3N7BHlfNlLPmlHkDan3h2z6Rlnvs7fPFV60nalYhCvrP5HIGwIgZFf/Z695AybKvCHTx5k3ZK2WN0Tzt2o8C83sxoX+Q2//EYmHUb2/N33p28A/LMZMCDhQfCPLh8awddNLaD8seUN+PM4bMtGZwrfnYhh6P9oRm0WKLLiEv208ixCTwsiWlEGJ2lM9hjrj8Nn4xK9OEIueLOJZdKLnq7gYRdluGcB3Ymkr2hT0I/TS01kiCo/9oi/AyoFcTqWt0PdBYC81p1kOZ2LpLF689eWABlQU4LBcik0twHrZHlBtNls3vRQWTz8HOi4qhYhbpWA/uvENL8D71Gl5sn1WnxVbT++RGbeN9//BTwAAXre/iC9h6wzxKCSehfbfArmAmMvw4mH85p8exOtQbAbKTU0DePETBapz5fB42ffni3tiKm4DFz+exaWmi5WR7HJG9+bmQPKie+vYgFh55+UrUVIteCq6t61fiX0+n+jeOqc6DquIZCYujXFBjIGSBtSponuvuYpP9v3Z2+dCprS9918TKV5JVgluyYoLVB5zbrl5eUr0ICbdV0UYV+V1LZDvHc+HfKv2u+TfXj9VzPBEiq/+h1/Fa3//DgAF50cuSzeZyTueD5ngSUHReV8v9Gti6ey24lnsiM3iYptO++UC5f2UKcuTl3XzsgFiUhr4lJxLsgvv/2/v6kLjOq7wt7Vsr+otbK0tslfEWhRT3BLstK4SEjdQLIop2I8mEPxQsAtNoBTShz6kj/JDH1oIggbqBNTYFIyhGNxg2uI8xTGpU5NAqVvXXlaq5UiNHESQXCndZvsw98z95tyZu9eObW2k+V6k3Z07f/femTPnfOcc33ehYCwAMlaPUBtsjZCxM/FJk8BCJkpdv8yFLASyk2rCGuDXWQDwWhGkLS35aWnSpzOShTuUL1WPgSnbgGu25UVEyGpiURDooDvSB9FFcEAbnZRYjmv6GQPgbG5Sp/x/bbyKf35nEoA5nshcyULK8US1fkPnoY3WkIiIiAeCnpAsYmLkmBh5NRMjc3Cee02M/NQv/wwAuPz4Bnsc06QunStFoKVXH8HrG7+7gdN/2+skRn7jrbMYO3I0wzGa+enThoy2+yb6fmB0NdfGq/jqzxZSJS7Me3Jxw+v4+B9z6/cYshbRzTzcywj5m9zNWEKerr56tb7D9z2QzffKR9OQ70roWJZH9/YR3rRfjfZ8BVJWK+uktBn9wqnXMHbkaNDr1SGGvTSH9vFBp+zdJBnqaWvIveJ+6Cx8HIc8nUW3OBECH4OPH2if0xsHuwmNzacY9O2yms/g4yzkMS0ZoksA3BR/2h8hw3totjKxMHyMW24nRF2XXRxwWbMCy7AFsOihaNeQQnQSNQBtuKZaG5xYWbPkRWY/Eq4PMNaJtow5GRs/R9PHqg4PYz6RGmtXzOfGuf86DFwe79iRo7hw6jU8etqE6BPKu/WdoWdsfvIRYMhVuD6MvCFrHvbhBYKBZnR5RjcFZ99Iw3IUnBc8qUcUgrXEsYl3E71g5LWxkJg+WcG5MLQJlcaA5VUAwHAr60DFPAqNxaFN6HuzZTkKlZm0vGjxZZHSmb4FfJzoa912FjVWcHIdMjdiJm4jPRKwMldHvpbPOhamm77wKWehEdNq30jD+GHAzSnK9cl9k6RBRtG5zZbRjoAcM3Pz+1/E1OE0eE3t4geoXVRHx5ZZ4BeHNtljIs6Y+/Do6R/ixrMmVeKTV57HR9/7DyoqGnm5ZSQUE+LPVXQWRVRwRkREFMKalCy6eTn6QsLZ3ZQkAmYRmr9wXKX52jwyVDdqt6QdbCPdoZjx6BPNi7ZhTXb0XRruzSU6dYPbhumPNpm2my3LpJU2K4mC0kfYYsg8MpFO91n6Ye8THWmknaruayLFlVtZic/MT3qUEompDKBxbsD2S66RMYiiWN83kapY2V1u3U4zhSUQnsxyY8BRsvKzJNfrREFiHm033wP2bceuiVk8eeV5AMA7P3/FKj2F6Sv17pow0sqFU68AAA5cPYjO68WIbUCPLBafle79IEhZCyFSltLUy/XXNSkL6E7KIop5t3gVHIdhy60OauR1WoiUlfxtJol5ZDwspoq465Kywn3i2A8CdpeW+3D90EYAHcx/0/hK9M9+wY6hecyklclrBzCWCMPEBOYnH0ENfkWpZouasaXBekRHIRYaPoZUB+dROT5oFxLLv6AXXawel158wknfyM+JWC/+9Itvp3OV6HMWrHJxwD6v7A7Pmw7zNKQtJnuJJY7HIDqMsSNHMT1exdbzsPVOHa478T4BoPMlf+pQH3raGlKE/ORTimkXdG3e005TPmelULtFHMUAVwPucyUH4F3EGGKKzDMLS7mQizn3B0Bw8WXTp+xkwur05TqVOrvFcZDvfPPIZlytmOR75jP3cj+LmE4lfoUTmuClOWtaFIjpkduTvrUObcxE/ZL6fEpd5/k90UZzruYE1g0RsthczXOwsueOowiVcTGkPlF6SlrFZ7/+F1x68YnM3L7dnMBKc+bzYw0JSRYL9DCLYs4nWXR7oVirjZGGsyPKqt5utuxuBaRSTcZk13BFU82z4B3G2OXrGZ6F7PJa1AayKQjZd0XGVD15Cdi33Xo1chshRzJ5oYRbwMccaZclCgBYqte9PAtdJ9fBPAuW3obPpOVsH0fr9t6EXh45kvDiUD15CYOXs5wMvTgJ+mfrGVZnuXUbU3/cgS37Os4OXm227PMgqFE9wljVnB1ROpbPmFynczQPy40BLE5uwmYy00pfZdGWueT5Zh7Kyp47VhEqdezcfRMLVzi1MND3ZgvT41Wr9BQp4uzoM8CoqWfkuWRsjQGgXVxtGSWL+yRZhKjiPvdybg9wzWg+nwh9xOLfpe2ieTF5LnwEID023w7uk6p83ATNKchzUdfxQvl7HS8UcHOXar8aPefcBy2x+LKB6fiV0kboPjL4Wj1eHYOT62HkHavZomR1Jso3BDAL/o5XN2QC6Ohyd8OziNaQiIiIQuiJY8haAJOKAHItB6xSS5OFZJeoBXgSDLacAHIMS12+Fzwu28ITAfUhzx3eSjJDrn5D913Aklgoo7fUIzEsbH2ii6D++OagMvMJluqbszEkR+uOAhIw1pewzuKTjBQg8R54Tsw817MMzP17bYRwAUspciRsnGM2aColVclblaElY18U7nIyhp27/4Ua6ViujVex49W9zpFSJNzv/uQGzo4+g8defgFAGkBHW4RK212dRx56+hgSkUL7pDwodGOjFrleQx+7RCTn2Ard4POAlZfM597vU676jpnssp+nrNXHOSDVa7G/hS/xEKdDkOvZz2Rlz51McmYAjg5p5Ln37BhZWS0u58JM3TUxmwnIJGN5462zeOzlFxwd2ts3T66vSFnrIZ6FRPvC6NMPPJ6F2ObvJZ7Fddp9pYzZdbcBEMe3dJ6av30cQPd4FtcPbcTO3WZ3XpxMXffLrTSVI/dFFNEiXel5kYVj6/n+1HT6/TbaxwftuBwqe9K3UTKdCobP3MI8MUxrFz9wTKe7JmYtF0MkPJZ0plBHZYYlEgORPLee77cRrton2sCk+X3qcB2Dx1eAIVirx3JjwJpHV/bcweKt/qReo7f4649/hbHLqen0038XXwKiZBFA6KXtFudTl8sja2m/lLzy+mEPRfHu1lbouKB3aSmrlcPcHx27IuS34gt3z9dI+3mLhf7suw++REuheeBjIy8KfL2z+NA1gEvKkt8A1wTsS5Tk+Gp4zOs6SZRApDCmvYv3MW8cTMri8jwmUXoeuHoQ7+z/DT7ufBQVnBEREfcPPXEM+azo5r7t+90nCRQxkeq6fG7SAIJBc9nkxtf66mUP0bKqA56dUx8NuO9Sf8gxTDgkTp/INMvZw6U/bWrD/u/pVxvuWVz6ys502hvWx+1wvmtkw9L1JUpFrXdhb1pm6C7VSxhupeK+ZEITL18bmBhZZz3La0mkL/ltqV5C9WTWkY2dAmW8lWQMy/v32j5UacycWrKSHKccAppHCjtw9aAzR7osl/nD136PL292Tal56IljSF6SIZ9SS7MXffZuH6ORy/rSuvk4GbptwA1zphcAKZsG7M2yErmPALz8Aq130d///UfbcsPIa4TmVL/A08eyAXt98xiaL9+9CC1iRpu/wSnrq0OzOyUatk5qJL9l3PJPtDNMTSANiqP1SBwBm+dO4kHwGHjeAKN70UfGcsvEGmUWpW/MgD/oksyVXG8X6BOpjsWONdG7TR/7Hz6dK6desQlbVSuEP9wygXffX/78BL8plUofAlgCML/afXnIqCGOeT2gl8c83Ol0vlKkYE8sFgBQKpXe7XQ631rtfjxMxDGvD6yVMUcFZ0RERCHExSIiIqIQemmx+PVqd2AVEMe8PrAmxtwzOouIiIjeRi9JFhERET2MuFhEREQUQlwsIiIiCiEuFhEREYUQF4uIiIhC+D/6uMRPO7llogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fddf88e0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 2/13530 [00:00<14:18, 15.75it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                            \n",
      "4. Getting final performances...\n",
      "\n",
      "best_params_out:  [{'base_kernel': 'edge', 'height': 4.0}]\n",
      "best_params_in:  [{'alpha': 39.810717055349734}]\n",
      "best_val_perf:  35.13329422064896\n",
      "best_val_std:  0.7832828899376076\n",
      "final_performance:  37.697602888271526\n",
      "final_confidence:  6.917076511859134\n",
      "train_performance:  30.10471658333036\n",
      "train_std:  0.5666874899231609\n",
      "time to calculate gram matrix:  4.349034786224365 s\n",
      "\n",
      "params                                                        train_perf        valid_perf          test_perf             gram_matrix_time\n",
      "------------------------------------------------------------  ----------------  ------------------  ------------------  ------------------\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 0.0}   49.55±29.71       84.81±62.30         83.10±69.42                       0.84\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 1.0}   57.14±115.85      101.64±220.27       115.57±320.98                     1.8\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 2.0}   41.17±15.40       69.60±33.17         69.69±38.03                       2.57\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 3.0}   42.49±13.37       71.04±30.47         65.16±28.23                       3.47\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 4.0}   70.30±107.52      137.29±269.14       119.54±173.83                     4.35\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 5.0}   40.83±12.73       70.09±37.93         65.95±34.58                       5.18\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 6.0}   53.81±67.21       93.79±126.55        105.99±195.09                     6.32\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 7.0}   41.18±11.58       65.63±20.34         68.25±31.41                       7.08\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 8.0}   808.40±4147.37    1438.75±7364.03     2314.42±12184.30                  7.86\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 9.0}   62.14±96.18       98.99±147.17        109.28±213.74                     8.73\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'edge', 'height': 10.0}  40.00±10.65       67.47±25.95         64.36±27.30                       9.4\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 0.0}   58.16±73.04       101.23±140.79       86.85±74.37                       0.84\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 1.0}   41.53±32.22       71.26±67.01         72.57±90.24                       1.8\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 2.0}   41.44±16.81       70.03±35.29         70.07±39.51                       2.57\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 3.0}   42.28±12.90       70.45±29.62         64.36±26.89                       3.47\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 4.0}   57.66±66.16       104.73±147.17       100.94±129.40                     4.35\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 5.0}   40.57±12.36       69.61±37.82         65.47±34.41                       5.18\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 6.0}   55.93±77.91       99.94±160.71        110.97±222.84                     6.32\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 7.0}   40.97±10.94       65.36±19.91         67.73±29.94                       7.08\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 8.0}   657.80±3301.60    1169.35±5860.66     1863.72±9701.59                   7.86\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 9.0}   388.32±1873.02    591.13±2826.58      844.72±4225.81                    8.73\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'edge', 'height': 10.0}  39.83±10.31       67.10±25.07         64.05±26.55                       9.4\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 0.0}   43.76±14.02       73.03±27.84         70.54±34.05                       0.84\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 1.0}   38.74±18.63       65.57±45.38         64.93±52.75                       1.8\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 2.0}   42.17±20.25       71.35±40.84         71.31±44.34                       2.57\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 3.0}   42.63±13.79       70.95±31.35         64.08±26.01                       3.47\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 4.0}   53.94±59.50       95.70±128.41        94.58±119.64                      4.35\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 5.0}   40.32±12.05       69.19±37.95         65.02±34.38                       5.18\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 6.0}   79.48±201.98      161.26±489.69       173.14±553.86                     6.32\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 7.0}   40.78±10.43       65.15±19.79         67.25±28.57                       7.08\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 8.0}   552.09±2629.44    977.82±4664.82      1520.27±7724.95                   7.86\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 9.0}   57.81±76.95       92.24±118.53        99.28±167.28                      8.73\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'edge', 'height': 10.0}  39.64±9.92        66.66±24.05         63.68±25.67                       9.4\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 0.0}   41.47±10.17       68.45±21.01         66.46±27.88                       0.84\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 1.0}   37.47±13.17       62.95±38.16         61.46±37.66                       1.8\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 2.0}   44.47±31.02       75.62±58.32         75.56±62.41                       2.57\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 3.0}   62.15±115.95      107.78±222.64       76.03±75.68                       3.47\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 4.0}   51.70±55.50       90.49±118.82        90.40±112.40                      4.35\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 5.0}   40.09±11.80       68.85±38.34         64.62±34.52                       5.18\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 6.0}   74.80±127.92      136.16±255.16       160.62±354.93                     6.32\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 7.0}   40.64±10.15       65.07±20.28         66.84±27.42                       7.08\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 8.0}   432.06±2094.85    766.41±3713.96      1207.84±6156.20                   7.86\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 9.0}   51.33±52.76       82.27±82.32         84.60±107.49                      8.73\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'edge', 'height': 10.0}  39.42±9.49        66.15±22.89         63.26±24.67                       9.4\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 0.0}   40.80±10.13       67.20±22.96         66.15±34.63                       0.84\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 1.0}   36.75±10.47       61.75±37.19         59.49±30.06                       1.8\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 2.0}   68.53±155.42      116.62±255.01       121.30±295.46                     2.57\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 3.0}   42.18±13.29       69.91±29.50         63.34±25.54                       3.47\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 4.0}   49.97±51.77       86.59±110.54        87.10±105.75                      4.35\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 5.0}   39.89±11.64       68.60±39.11         64.28±34.91                       5.18\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 6.0}   52.83±54.18       92.64±114.49        100.24±155.03                     6.32\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 7.0}   40.63±10.38       65.29±22.10         66.64±26.78                       7.08\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 8.0}   351.48±1668.81    622.36±2954.96      974.97±4903.80                    7.86\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 9.0}   49.43±49.84       79.23±77.33         80.18±97.69                       8.73\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'edge', 'height': 10.0}  39.17±9.01        65.57±21.61         62.77±23.54                       9.4\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 0.0}   48.67±40.85       88.00±110.23        95.36±177.25                      0.84\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 1.0}   36.57±10.21       63.00±48.53         58.92±28.47                       1.8\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 2.0}   141.74±535.13     421.03±1916.16      281.89±1139.95                    2.57\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 3.0}   42.60±17.73       71.25±39.40         66.20±40.82                       3.47\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 4.0}   48.51±47.97       83.36±102.30        84.38±99.85                       4.35\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 5.0}   39.73±11.62       68.50±40.42         64.06±35.71                       5.18\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 6.0}   54.50±60.43       94.65±118.10        99.93±144.77                      6.32\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 7.0}   40.96±11.99       66.31±27.46         67.00±27.60                       7.08\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 8.0}   287.98±1328.70    508.43±2348.07      789.78±3903.78                    7.86\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 9.0}   49.23±53.65       78.76±82.14         79.38±102.92                      8.73\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'edge', 'height': 10.0}  38.89±8.49        64.92±20.21         62.23±22.32                       9.4\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 0.0}   49.44±63.25       79.52±98.29         77.88±102.50                      0.84\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 1.0}   45.07±56.68       114.52±334.41       79.62±139.83                      1.8\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 2.0}   42.30±20.21       72.60±52.91         70.57±43.33                       2.57\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 3.0}   41.02±12.95       68.19±30.95         63.27±30.39                       3.47\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 4.0}   47.34±44.40       80.78±94.54         82.40±96.02                       4.35\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 5.0}   39.65±11.86       68.65±42.63         64.03±37.19                       5.18\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 6.0}   56.74±74.29       98.67±139.65        102.25±155.29                     6.32\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 7.0}   42.65±19.55       70.65±47.62         69.63±36.09                       7.08\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 8.0}   237.31±1056.74    417.19±1861.62      641.62±3103.70                    7.86\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 9.0}   51.11±67.03       81.51±100.96        82.68±126.69                      8.73\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'edge', 'height': 10.0}  38.59±7.95        64.22±18.74         61.63±21.02                       9.4\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 0.0}   37.62±7.05        60.25±15.43         57.95±18.94                       0.84\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 1.0}   34.90±5.62        55.49±18.04         54.58±17.16                       1.8\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 2.0}   42.39±22.91       68.96±35.54         69.44±42.92                       2.57\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 3.0}   39.92±11.32       66.10±30.42         60.90±25.06                       3.47\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 4.0}   47.00±42.40       80.36±89.93         82.68±98.50                       4.35\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 5.0}   39.76±12.60       69.33±46.49         64.43±39.94                       5.18\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 6.0}   46.95±37.09       82.85±90.89         85.94±114.36                      6.32\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 7.0}   87.95±246.72      181.50±605.37       140.96±376.77                     7.08\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 8.0}   196.68±838.86     343.67±1470.51      522.64±2462.15                    7.86\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 9.0}   61.55±123.59      97.78±183.18        102.23±230.34                     8.73\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'edge', 'height': 10.0}  38.26±7.41        63.47±17.24         61.01±19.72                       9.4\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 0.0}   36.53±5.41        58.47±14.84         55.98±17.17                       0.84\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 1.0}   34.24±4.26        52.78±9.87          52.80±13.71                       1.8\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 2.0}   41.71±17.28       69.36±35.58         69.82±41.67                       2.57\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 3.0}   41.65±23.26       71.56±66.99         64.53±47.71                       3.47\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 4.0}   47.56±48.46       81.23±101.74        85.92±126.16                      4.35\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 5.0}   40.49±14.97       71.52±54.18         66.05±45.65                       5.18\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 6.0}   45.51±35.45       82.69±102.60        84.75±121.47                      6.32\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 7.0}   41.31±15.17       67.49±35.37         67.26±31.21                       7.08\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 8.0}   164.00±664.11     284.16±1155.23      426.85±1946.93                    7.86\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 9.0}   93.79±301.93      145.03±442.81       161.85±559.00                     8.73\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'edge', 'height': 10.0}  37.95±6.91        62.72±15.77         60.41±18.54                       9.4\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 0.0}   36.70±8.18        61.08±35.01         58.21±32.69                       0.84\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 1.0}   33.85±3.65        51.64±8.74          51.71±11.96                       1.8\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 2.0}   38.22±10.21       61.12±17.69         61.28±23.58                       2.57\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 3.0}   42.88±33.18       75.39±95.49         67.07±67.25                       3.47\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 4.0}   75.31±200.80      138.80±418.48       166.02±571.13                     4.35\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 5.0}   201.63±885.83     377.62±1672.37      310.80±1337.66                    5.18\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 6.0}   46.24±40.84       89.38±141.83        89.62±151.18                      6.32\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 7.0}   38.68±7.93        61.23±15.47         62.25±21.59                       7.08\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 8.0}   137.71±524.18     235.89±901.05       349.75±1533.66                    7.86\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 9.0}   46.06±48.95       73.21±74.09         73.22±93.28                       8.73\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'edge', 'height': 10.0}  37.88±6.82        62.38±14.75         60.38±18.67                       9.4\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 0.0}   35.12±4.45        54.48±11.96         52.47±13.58                       0.84\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 1.0}   33.55±3.26        50.96±8.78          50.87±10.80                       1.8\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 2.0}   38.29±13.48       60.41±23.15         60.82±28.07                       2.57\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 3.0}   37.71±9.20        60.93±25.37         56.58±20.37                       3.47\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 4.0}   44.35±40.73       74.18±86.18         78.75±109.24                      4.35\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 5.0}   44.15±28.67       86.29±114.63        78.18±93.59                       5.18\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 6.0}   52.82±61.72       115.65±248.22       110.26±231.82                     6.32\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 7.0}   37.80±6.91        59.09±10.99         60.49±20.97                       7.08\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 8.0}   116.71±413.08     196.91±697.39       288.11±1204.79                    7.86\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 9.0}   40.48±22.25       64.38±35.33         62.74±43.74                       8.73\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'edge', 'height': 10.0}  37.40±6.26        61.58±13.69         59.27±16.87                       9.4\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 0.0}   34.91±4.94        53.69±13.55         51.62±13.93                       0.84\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 1.0}   33.33±3.00        50.51±9.21          50.24±10.15                       1.8\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 2.0}   35.94±8.31        55.55±14.44         55.86±20.51                       2.57\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 3.0}   36.45±5.88        57.56±14.97         54.03±14.90                       3.47\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 4.0}   38.81±18.25       62.38±39.52         64.02±43.13                       4.35\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 5.0}   86.97±270.81      269.55±1139.50      220.58±889.06                     5.18\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 6.0}   52.17±70.64       125.69±334.51       113.39±276.09                     6.32\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 7.0}   37.58±8.38        58.35±11.85         60.08±24.18                       7.08\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 8.0}   100.28±326.85     165.91±537.15       239.78±948.57                     7.86\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 9.0}   38.16±12.72       60.48±21.59         58.35±25.85                       8.73\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'edge', 'height': 10.0}  36.92±5.91        60.62±12.85         58.30±15.80                       9.4\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 0.0}   35.87±9.20        56.43±28.46         53.47±22.76                       0.84\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 1.0}   33.93±4.87        52.11±14.29         51.28±12.85                       1.8\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 2.0}   44.44±59.20       68.53±95.90         72.31±120.31                      2.57\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 3.0}   35.67±4.70        55.56±11.13         52.46±13.11                       3.47\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 4.0}   36.71±11.70       57.75±25.74         58.86±25.82                       4.35\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 5.0}   43.26±29.52       82.85±106.43        77.68±97.43                       5.18\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 6.0}   43.43±35.65       86.35±147.26        79.64±117.98                      6.32\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 7.0}   42.23±35.51       65.44±53.34         70.85±85.04                       7.08\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 8.0}   88.01±263.31      142.12±416.01       203.57±758.39                     7.86\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 9.0}   36.81±8.22        58.03±15.08         55.78±17.30                       8.73\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'edge', 'height': 10.0}  36.73±6.22        60.26±13.51         57.99±16.57                       9.4\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 0.0}   43.41±49.80       69.89±94.80         66.89±93.62                       0.84\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 1.0}   33.34±3.50        51.86±19.46         51.29±19.34                       1.8\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 2.0}   34.24±7.30        51.46±12.19         51.60±18.54                       2.57\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 3.0}   35.11±4.20        54.06±9.40          51.29±12.29                       3.47\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 4.0}   35.44±8.16        54.83±18.09         55.87±17.87                       4.35\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 5.0}   37.13±9.89        63.31±39.17         58.92±34.73                       5.18\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 6.0}   40.76±21.36       70.95±62.82         68.60±57.27                       6.32\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 7.0}   37.10±9.10        57.44±13.80         58.75±24.86                       7.08\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 8.0}   80.02±222.76      125.35±332.42       179.55±634.08                     7.86\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 9.0}   35.89±5.83        56.22±11.49         54.01±12.74                       8.73\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'edge', 'height': 10.0}  37.06±8.52        61.16±19.00         58.91±22.17                       9.4\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 0.0}   33.68±4.53        49.77±11.66         48.05±12.21                       0.84\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 1.0}   33.57±4.82        51.45±17.36         50.96±17.63                       1.8\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 2.0}   33.33±4.35        49.56±7.62          49.30±12.65                       2.57\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 3.0}   34.74±4.34        52.96±9.18          50.47±12.28                       3.47\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 4.0}   34.53±5.87        52.66±13.00         53.77±13.33                       4.35\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 5.0}   35.66±5.90        58.32±21.69         54.54±21.11                       5.18\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 6.0}   39.19±15.89       65.00±38.52         64.32±40.41                       6.32\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 7.0}   41.82±29.54       74.79±102.03        68.44±61.96                       7.08\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 8.0}   78.28±214.42      117.57±293.19       172.52±597.80                     7.86\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 9.0}   35.21±4.48        54.74±9.27          52.69±10.25                       8.73\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'edge', 'height': 10.0}  48.66±65.94       88.48±149.62        86.19±156.54                      9.4\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 0.0}   32.84±3.45        47.45±8.98          45.97±9.93                        0.84\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 1.0}   32.85±3.81        48.33±10.35         47.78±9.63                        1.8\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 2.0}   32.74±3.37        48.09±5.93          47.63±10.58                       2.57\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 3.0}   34.88±6.90        52.80±13.51         50.67±16.14                       3.47\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 4.0}   33.83±4.33        50.92±9.46          52.14±10.52                       4.35\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 5.0}   34.85±4.39        55.61±14.03         52.19±15.48                       5.18\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 6.0}   38.76±17.61       63.99±44.78         63.42±42.30                       6.32\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 7.0}   35.84±6.10        54.68±10.32         55.48±18.34                       7.08\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 8.0}   118.44±434.85     150.35±475.03       269.92±1131.99                    7.86\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 9.0}   34.73±3.81        53.55±7.77          51.73±9.11                        8.73\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'edge', 'height': 10.0}  37.85±12.29       64.25±35.11         61.33±32.87                       9.4\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 0.0}   32.46±3.85        46.22±9.82          44.84±10.43                       0.84\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 1.0}   32.13±2.43        46.15±7.62          45.69±6.90                        1.8\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 2.0}   32.22±2.78        46.63±4.57          46.13±9.46                        2.57\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 3.0}   38.27±27.22       58.52±50.15         57.35±55.15                       3.47\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 4.0}   33.27±3.31        49.45±6.97          50.80±8.73                        4.35\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 5.0}   34.28±3.77        53.72±10.22         50.55±12.80                       5.18\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 6.0}   35.46±8.81        55.30±18.13         55.98±25.44                       6.32\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 7.0}   36.41±11.35       54.13±11.66         56.72±29.76                       7.08\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 8.0}   85.02±258.70      122.18±337.54       196.44±747.08                     7.86\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 9.0}   34.66±4.83        52.93±7.83          51.51±10.82                       8.73\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'edge', 'height': 10.0}  35.23±5.65        56.99±15.26         54.86±14.99                       9.4\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 0.0}   32.58±6.55        46.26±16.67         44.88±16.20                       0.84\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 1.0}   31.79±2.15        44.89±6.48          44.52±6.27                        1.8\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 2.0}   31.89±2.42        45.58±3.88          45.02±8.59                        2.57\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 3.0}   33.69±4.52        49.78±9.01          47.79±11.38                       3.47\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 4.0}   32.80±2.64        48.16±5.23          49.64±7.58                        4.35\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 5.0}   33.81±3.41        52.18±8.13          49.22±11.34                       5.18\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 6.0}   34.72±7.93        53.32±15.83         53.91±22.54                       6.32\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 7.0}   36.73±14.06       54.36±15.57         57.42±36.23                       7.08\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 8.0}   376.05±1858.91    479.48±2310.42      786.84±3996.13                    7.86\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 9.0}   36.42±16.11       55.03±22.96         54.49±30.03                       8.73\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'edge', 'height': 10.0}  34.01±3.60        53.52±7.92          51.89±9.13                        9.4\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 0.0}   37.15±33.43       57.40±83.93         55.26±78.26                       0.84\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 1.0}   31.52±1.89        43.80±5.39          43.52±5.85                        1.8\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 2.0}   31.62±2.13        44.66±3.36          44.02±7.89                        2.57\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 3.0}   33.01±2.80        48.09±5.73          46.19±9.06                        3.47\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 4.0}   32.42±2.23        47.01±4.04          48.62±6.80                        4.35\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 5.0}   33.40±3.05        50.79±6.75          48.01±10.32                       5.18\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 6.0}   33.75±5.94        51.17±12.09         51.74±18.74                       6.32\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 7.0}   36.13±11.20       57.72±40.70         56.79±37.15                       7.08\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 8.0}   69.54±183.74      101.10±248.10       151.51±525.85                     7.86\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 9.0}   33.38±2.77        50.13±5.38          48.92±7.32                        8.73\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'edge', 'height': 10.0}  33.37±3.12        51.70±6.40          50.38±7.73                        9.4\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 0.0}   31.23±3.10        42.36±7.91          41.22±8.51                        0.84\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 1.0}   31.27±1.64        42.78±4.38          42.60±5.56                        1.8\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 2.0}   31.37±1.88        43.81±2.94          43.10±7.30                        2.57\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 3.0}   32.58±2.19        46.86±4.45          45.08±8.29                        3.47\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 4.0}   32.08±1.97        45.97±3.24          47.70±6.26                        4.35\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 5.0}   33.01±2.63        49.48±5.63          46.86±9.47                        5.18\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 6.0}   9372.38±51155.15  31477.18±172141.77  19469.68±106369.85                6.32\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 7.0}   34.52±5.69        52.16±15.67         51.49±18.34                       7.08\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 8.0}   56.76±116.80      86.41±176.62        119.21±357.28                     7.86\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 9.0}   32.80±2.16        48.72±4.59          47.72±6.28                        8.73\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'edge', 'height': 10.0}  32.88±2.86        50.28±5.72          49.24±7.09                        9.4\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 0.0}   30.58±1.42        40.47±3.34          39.50±5.48                        0.84\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 1.0}   31.03±1.41        41.82±3.50          41.73±5.35                        1.8\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 2.0}   31.14±1.67        43.00±2.60          42.21±6.80                        2.57\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 3.0}   32.24±1.87        45.77±3.69          44.13±7.81                        3.47\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 4.0}   31.79±1.80        45.00±2.73          46.83±5.86                        4.35\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 5.0}   32.66±2.23        48.23±4.68          45.78±8.76                        5.18\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 6.0}   32.54±3.86        48.04±8.20          48.43±13.78                       6.32\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 7.0}   35.43±13.34       54.21±34.41         52.49±30.47                       7.08\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 8.0}   42.34±40.70       63.07±57.42         74.50±120.54                      7.86\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 9.0}   32.41±2.01        47.59±4.24          46.84±5.96                        8.73\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'edge', 'height': 10.0}  32.48±2.65        49.03±5.22          48.29±6.73                        9.4\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 0.0}   30.20±0.96        39.26±1.91          38.44±4.94                        0.84\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 1.0}   30.78±1.21        40.89±2.79          40.88±5.22                        1.8\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 2.0}   30.92±1.47        42.20±2.31          41.35±6.39                        2.57\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 3.0}   31.93±1.65        44.74±3.15          43.25±7.40                        3.47\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 4.0}   31.52±1.67        44.08±2.39          46.01±5.55                        4.35\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 5.0}   32.34±1.88        47.06±3.92          44.75±8.21                        5.18\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 6.0}   32.13±3.08        46.79±6.49          47.06±11.89                       6.32\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 7.0}   34.24±9.14        50.83±22.84         49.45±21.89                       7.08\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 8.0}   41.68±38.43       61.27±53.30         71.75±109.62                      7.86\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 9.0}   32.11±1.92        46.62±3.92          46.08±5.77                        8.73\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'edge', 'height': 10.0}  32.13±2.45        47.88±4.77          47.43±6.49                        9.4\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 0.0}   29.93±0.77        38.31±1.32          37.64±4.83                        0.84\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 1.0}   30.54±1.04        40.01±2.22          40.07±5.15                        1.8\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 2.0}   30.70±1.29        41.43±2.07          40.51±6.07                        2.57\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 3.0}   31.64±1.48        43.75±2.72          42.40±7.03                        3.47\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 4.0}   31.26±1.57        43.19±2.16          45.22±5.31                        4.35\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 5.0}   32.05±1.61        45.95±3.32          43.78±7.78                        5.18\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 6.0}   31.79±2.34        46.11±6.75          45.99±11.08                       6.32\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 7.0}   32.86±3.58        47.02±7.89          46.09±11.64                       7.08\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 8.0}   39.81±25.84       59.29±43.25         67.33±79.38                       7.86\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 9.0}   31.87±1.81        45.76±3.55          45.37±5.59                        8.73\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'edge', 'height': 10.0}  31.81±2.25        46.79±4.32          46.66±6.32                        9.4\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 0.0}   29.73±0.66        37.51±1.05          37.02±4.81                        0.84\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 1.0}   30.30±0.90        39.17±1.80          39.30±5.13                        1.8\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 2.0}   30.48±1.13        40.66±1.86          39.69±5.82                        2.57\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 3.0}   31.36±1.34        42.79±2.36          41.58±6.68                        3.47\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 4.0}   31.01±1.46        42.32±1.98          44.45±5.12                        4.35\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 5.0}   31.79±1.41        44.88±2.86          42.85±7.45                        5.18\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 6.0}   31.21±1.95        43.65±3.49          43.94±8.38                        6.32\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 7.0}   32.33±2.38        45.48±4.68          44.55±9.49                        7.08\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 8.0}   35.69±11.60       51.66±18.31         55.02±36.75                       7.86\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 9.0}   31.65±1.70        44.92±3.21          44.69±5.42                        8.73\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'edge', 'height': 10.0}  31.53±2.07        45.76±3.90          45.92±6.19                        9.4\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 0.0}   29.59±0.59        36.84±0.91          36.53±4.82                        0.84\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 1.0}   30.08±0.79        38.38±1.49          38.57±5.15                        1.8\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 2.0}   30.26±0.99        39.91±1.70          38.89±5.66                        2.57\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 3.0}   31.07±1.21        41.84±2.05          40.77±6.35                        3.47\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 4.0}   30.77±1.34        41.47±1.81          43.68±5.00                        4.35\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 5.0}   31.53±1.25        43.86±2.49          41.94±7.16                        5.18\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 6.0}   30.93±1.65        42.56±2.94          42.81±7.34                        6.32\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 7.0}   31.96±1.83        44.34±3.30          43.35±8.41                        7.08\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 8.0}   34.01±6.61        48.24±9.99          49.84±21.37                       7.86\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 9.0}   31.44±1.59        44.09±2.91          44.00±5.27                        8.73\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'edge', 'height': 10.0}  31.27±1.88        44.76±3.46          45.25±6.06                        9.4\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 0.0}   29.53±0.56        36.30±0.84          36.15±4.86                        0.84\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 1.0}   29.88±0.70        37.66±1.27          37.91±5.19                        1.8\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 2.0}   30.05±0.87        39.19±1.56          38.13±5.57                        2.57\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 3.0}   30.79±1.09        40.92±1.78          39.98±6.06                        3.47\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 4.0}   30.52±1.22        40.63±1.65          42.94±4.94                        4.35\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 5.0}   31.27±1.11        42.86±2.17          41.04±6.90                        5.18\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 6.0}   30.69±1.41        41.64±2.48          41.76±6.59                        6.32\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 7.0}   31.64±1.48        43.34±2.52          42.28±7.65                        7.08\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 8.0}   33.30±5.16        46.63±8.43          47.41±15.32                       7.86\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 9.0}   31.23±1.48        43.26±2.63          43.29±5.13                        8.73\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'edge', 'height': 10.0}  31.02±1.70        43.80±3.07          44.58±5.98                        9.4\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 0.0}   29.56±0.54        35.87±0.79          35.88±4.91                        0.84\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 1.0}   29.72±0.64        37.02±1.13          37.31±5.25                        1.8\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 2.0}   29.87±0.78        38.49±1.44          37.41±5.55                        2.57\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 3.0}   30.51±0.98        40.03±1.55          39.22±5.81                        3.47\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 4.0}   30.27±1.11        39.81±1.50          42.20±4.93                        4.35\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 5.0}   31.01±0.99        41.89±1.90          40.15±6.65                        5.18\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 6.0}   30.47±1.23        40.79±2.13          40.78±6.09                        6.32\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 7.0}   31.34±1.21        42.40±2.00          41.28±7.02                        7.08\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 8.0}   73.50±226.84      132.22±483.48       127.18±453.61                     7.86\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 9.0}   31.02±1.37        42.43±2.38          42.56±5.00                        8.73\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'edge', 'height': 10.0}  30.79±1.53        42.88±2.73          43.92±5.91                        9.4\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 0.0}   29.67±0.53        35.57±0.77          35.72±4.98                        0.84\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 1.0}   29.60±0.59        36.46±1.03          36.78±5.33                        1.8\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 2.0}   29.71±0.71        37.85±1.34          36.74±5.58                        2.57\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 3.0}   30.25±0.88        39.18±1.36          38.49±5.62                        3.47\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 4.0}   30.03±1.00        39.02±1.36          41.49±4.98                        4.35\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 5.0}   30.75±0.88        40.94±1.66          39.27±6.42                        5.18\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 6.0}   30.26±1.09        40.00±1.87          39.86±5.78                        6.32\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 7.0}   31.04±0.99        41.50±1.61          40.34±6.48                        7.08\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 8.0}   32.01±2.98        43.41±4.78          43.49±9.02                        7.86\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 9.0}   30.81±1.26        41.61±2.14          41.82±4.87                        8.73\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'edge', 'height': 10.0}  30.56±1.38        41.99±2.42          43.27±5.85                        9.4\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 0.0}   29.90±0.52        35.39±0.76          35.65±5.07                        0.84\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 1.0}   29.55±0.56        35.99±0.96          36.32±5.43                        1.8\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 2.0}   29.59±0.66        37.25±1.26          36.13±5.65                        2.57\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 3.0}   30.02±0.79        38.39±1.22          37.81±5.48                        3.47\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 4.0}   29.81±0.90        38.26±1.24          40.81±5.08                        4.35\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 5.0}   30.49±0.78        40.04±1.45          38.41±6.19                        5.18\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 6.0}   30.07±0.97        39.25±1.66          39.01±5.62                        6.32\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 7.0}   30.76±0.80        40.62±1.30          39.43±6.01                        7.08\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 8.0}   31.40±1.90        41.88±2.61          41.71±6.84                        7.86\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 9.0}   30.59±1.15        40.81±1.92          41.07±4.75                        8.73\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'edge', 'height': 10.0}  30.34±1.24        41.12±2.15          42.62±5.81                        9.4\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 0.0}   30.26±0.52        35.36±0.75          35.70±5.19                        0.84\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 1.0}   29.57±0.54        35.63±0.91          35.94±5.57                        1.8\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 2.0}   29.52±0.63        36.72±1.19          35.59±5.76                        2.57\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 3.0}   29.82±0.72        37.67±1.11          37.17±5.41                        3.47\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 4.0}   29.61±0.81        37.57±1.13          40.17±5.23                        4.35\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 5.0}   30.24±0.68        39.19±1.28          37.58±5.99                        5.18\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 6.0}   29.89±0.88        38.55±1.50          38.22±5.57                        6.32\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 7.0}   30.48±0.64        39.78±1.07          38.58±5.61                        7.08\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 8.0}   30.97±1.42        40.74±1.84          40.38±5.79                        7.86\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 9.0}   30.36±1.03        40.02±1.72          40.32±4.63                        8.73\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'edge', 'height': 10.0}  30.12±1.12        40.28±1.92          41.97±5.79                        9.4\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 0.0}   30.77±0.52        35.49±0.75          35.87±5.34                        0.84\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 1.0}   29.69±0.53        35.37±0.88          35.65±5.74                        1.8\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 2.0}   29.52±0.61        36.27±1.13          35.13±5.89                        2.57\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 3.0}   29.68±0.67        37.04±1.02          36.60±5.38                        3.47\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 4.0}   29.46±0.74        36.93±1.04          39.58±5.40                        4.35\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 5.0}   30.03±0.61        38.40±1.14          36.80±5.79                        5.18\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 6.0}   29.73±0.80        37.90±1.38          37.51±5.58                        6.32\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 7.0}   30.22±0.51        38.98±0.91          37.78±5.28                        7.08\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 8.0}   30.60±1.12        39.76±1.42          39.25±5.21                        7.86\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 9.0}   30.14±0.93        39.27±1.54          39.57±4.52                        8.73\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'edge', 'height': 10.0}  29.91±1.01        39.48±1.72          41.32±5.78                        9.4\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 0.0}   31.45±0.51        35.81±0.75          36.19±5.53                        0.84\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 1.0}   29.91±0.53        35.24±0.85          35.45±5.94                        1.8\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 2.0}   29.60±0.60        35.91±1.08          34.75±6.05                        2.57\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 3.0}   29.61±0.62        36.50±0.95          36.10±5.39                        3.47\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 4.0}   29.35±0.69        36.38±0.97          39.06±5.61                        4.35\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 5.0}   29.85±0.56        37.69±1.05          36.08±5.61                        5.18\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 6.0}   29.61±0.73        37.30±1.28          36.88±5.63                        6.32\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 7.0}   30.00±0.41        38.24±0.80          37.05±5.03                        7.08\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 8.0}   30.28±0.90        38.90±1.16          38.28±4.90                        7.86\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 9.0}   29.94±0.83        38.54±1.38          38.85±4.44                        8.73\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'edge', 'height': 10.0}  29.71±0.92        38.71±1.55          40.68±5.79                        9.4\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 0.0}   32.35±0.51        36.34±0.74          36.67±5.75                        0.84\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 1.0}   30.26±0.52        35.25±0.82          35.36±6.17                        1.8\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 2.0}   29.77±0.59        35.65±1.04          34.48±6.23                        2.57\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 3.0}   29.61±0.59        36.06±0.89          35.67±5.43                        3.47\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 4.0}   29.32±0.65        35.91±0.92          38.60±5.84                        4.35\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 5.0}   29.73±0.52        37.07±0.98          35.44±5.44                        5.18\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 6.0}   29.53±0.67        36.77±1.19          36.34±5.70                        6.32\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 7.0}   29.82±0.36        37.56±0.74          36.39±4.84                        7.08\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 8.0}   30.02±0.74        38.13±1.00          37.45±4.76                        7.86\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 9.0}   29.75±0.74        37.86±1.24          38.17±4.37                        8.73\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'edge', 'height': 10.0}  29.53±0.85        37.99±1.41          40.05±5.81                        9.4\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 0.0}   33.51±0.50        37.13±0.74          37.36±6.01                        0.84\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 1.0}   30.77±0.52        35.42±0.80          35.40±6.43                        1.8\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 2.0}   30.06±0.59        35.51±1.01          34.33±6.42                        2.57\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 3.0}   29.71±0.57        35.75±0.84          35.33±5.49                        3.47\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 4.0}   29.35±0.62        35.54±0.87          38.22±6.09                        4.35\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 5.0}   29.68±0.50        36.55±0.93          34.89±5.27                        5.18\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 6.0}   29.50±0.63        36.31±1.12          35.89±5.79                        6.32\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 7.0}   29.69±0.34        36.96±0.71          35.82±4.71                        7.08\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 8.0}   29.82±0.63        37.46±0.89          36.74±4.72                        7.86\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 9.0}   29.60±0.67        37.24±1.12          37.54±4.34                        8.73\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'edge', 'height': 10.0}  29.38±0.79        37.34±1.29          39.45±5.83                        9.4\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 0.0}   54.26±26.40       54.52±22.51         56.61±28.88                       0.84\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 1.0}   31.46±0.52        35.77±0.79          35.60±6.73                        1.8\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 2.0}   30.47±0.59        35.51±0.99          34.32±6.63                        2.57\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 3.0}   29.93±0.55        35.56±0.79          35.09±5.56                        3.47\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 4.0}   29.49±0.60        35.27±0.84          37.93±6.35                        4.35\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 5.0}   29.72±0.49        36.15±0.89          34.44±5.10                        5.18\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 6.0}   29.54±0.59        35.93±1.06          35.54±5.90                        6.32\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 7.0}   29.64±0.34        36.46±0.69          35.33±4.64                        7.08\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 8.0}   29.69±0.55        36.89±0.82          36.17±4.73                        7.86\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 9.0}   29.50±0.62        36.68±1.01          36.98±4.33                        8.73\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'edge', 'height': 10.0}  29.28±0.74        36.75±1.19          38.89±5.85                        9.4\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 0.0}   38.48±1.99        40.61±2.06          40.03±6.61                        0.84\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 1.0}   32.36±0.52        36.32±0.78          35.99±7.05                        1.8\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 2.0}   31.04±0.59        35.67±0.97          34.47±6.86                        2.57\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 3.0}   30.28±0.53        35.52±0.75          34.97±5.64                        3.47\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 4.0}   29.73±0.58        35.13±0.81          37.75±6.63                        4.35\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 5.0}   29.86±0.49        35.87±0.85          34.10±4.94                        5.18\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 6.0}   29.66±0.56        35.65±1.00          35.30±6.02                        6.32\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 7.0}   29.67±0.36        36.05±0.68          34.94±4.61                        7.08\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 8.0}   29.65±0.50        36.43±0.77          35.73±4.79                        7.86\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 9.0}   29.46±0.58        36.21±0.92          36.50±4.37                        8.73\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'edge', 'height': 10.0}  29.24±0.70        36.24±1.12          38.37±5.87                        9.4\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 0.0}   39.37±0.82        41.50±1.16          40.87±6.85                        0.84\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 1.0}   34.71±6.61        38.17±5.82          38.07±9.75                        1.8\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 2.0}   31.79±0.59        36.02±0.96          34.83±7.11                        2.57\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 3.0}   30.79±0.52        35.65±0.71          34.97±5.74                        3.47\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 4.0}   30.10±0.57        35.13±0.78          37.70±6.92                        4.35\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 5.0}   30.12±0.48        35.72±0.81          33.90±4.79                        5.18\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 6.0}   29.87±0.53        35.48±0.94          35.18±6.17                        6.32\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 7.0}   29.79±0.37        35.76±0.67          34.65±4.63                        7.08\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 8.0}   29.70±0.47        36.07±0.74          35.43±4.88                        7.86\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 9.0}   29.50±0.55        35.83±0.84          36.11±4.43                        8.73\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'edge', 'height': 10.0}  29.26±0.67        35.82±1.06          37.93±5.87                        9.4\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 0.0}   41.78±0.74        43.71±1.10          42.98±7.21                        0.84\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 1.0}   53.47±24.05       56.16±26.70         54.78±25.25                       1.8\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 2.0}   32.76±0.59        36.59±0.95          35.41±7.37                        2.57\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 3.0}   31.47±0.51        35.96±0.67          35.13±5.84                        3.47\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 4.0}   30.63±0.56        35.29±0.76          37.79±7.20                        4.35\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 5.0}   30.52±0.47        35.73±0.77          33.83±4.67                        5.18\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 6.0}   30.20±0.50        35.44±0.89          35.19±6.34                        6.32\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 7.0}   30.03±0.39        35.59±0.66          34.47±4.69                        7.08\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 8.0}   29.86±0.46        35.83±0.71          35.29±4.98                        7.86\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 9.0}   29.63±0.54        35.56±0.76          35.84±4.54                        8.73\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'edge', 'height': 10.0}  29.37±0.65        35.52±1.02          37.58±5.87                        9.4\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 0.0}   44.96±0.73        46.69±1.07          45.86±7.54                        0.84\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 1.0}   38.68±2.24        40.88±2.18          39.95±8.19                        1.8\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 2.0}   36.00±7.12        39.78±8.77          38.26±9.07                        2.57\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 3.0}   32.37±0.49        36.49±0.63          35.46±5.97                        3.47\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 4.0}   31.33±0.55        35.63±0.74          38.05±7.49                        4.35\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 5.0}   31.08±0.47        35.90±0.73          33.93±4.59                        5.18\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 6.0}   30.66±0.48        35.54±0.84          35.34±6.54                        6.32\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 7.0}   30.40±0.40        35.57±0.65          34.42±4.80                        7.08\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 8.0}   30.15±0.45        35.73±0.69          35.30±5.11                        7.86\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 9.0}   29.87±0.53        35.41±0.70          35.68±4.67                        8.73\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'edge', 'height': 10.0}  29.59±0.64        35.32±0.98          37.33±5.87                        9.4\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 0.0}   48.89±0.71        50.43±1.05          49.52±7.79                        0.84\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 1.0}   39.52±0.76        41.71±0.90          40.53±8.79                        1.8\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 2.0}   70.21±65.19       69.38±59.73         68.55±60.59                       2.57\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 3.0}   33.52±0.48        37.27±0.60          36.00±6.12                        3.47\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 4.0}   32.25±0.54        36.18±0.72          38.53±7.77                        4.35\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 5.0}   31.82±0.46        36.27±0.69          34.22±4.59                        5.18\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 6.0}   31.28±0.47        35.80±0.79          35.66±6.78                        6.32\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 7.0}   30.92±0.40        35.71±0.63          34.51±4.95                        7.08\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 8.0}   30.58±0.46        35.77±0.67          35.49±5.25                        7.86\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 9.0}   30.24±0.52        35.41±0.65          35.67±4.82                        8.73\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'edge', 'height': 10.0}  29.92±0.62        35.27±0.94          37.22±5.86                        9.4\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 0.0}   53.55±0.70        54.91±1.02          53.96±7.97                        0.84\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 1.0}   41.96±0.66        43.95±0.83          42.75±9.13                        1.8\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 2.0}   38.44±1.04        40.51±1.29          40.16±8.00                        2.57\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 3.0}   99.24±99.72       92.78±86.06         95.93±95.47                       3.47\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 4.0}   33.61±1.13        37.24±1.34          39.38±8.20                        4.35\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 5.0}   32.79±0.44        36.86±0.66          34.71±4.71                        5.18\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 6.0}   32.08±0.45        36.25±0.74          36.17±7.04                        6.32\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 7.0}   31.62±0.40        36.04±0.61          34.77±5.17                        7.08\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 8.0}   31.18±0.46        35.98±0.65          35.87±5.40                        7.86\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 9.0}   30.77±0.52        35.57±0.60          35.81±4.99                        8.73\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'edge', 'height': 10.0}  30.39±0.61        35.36±0.91          37.26±5.87                        9.4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "calculate performance: 100%|██████████| 13530/13530 [12:40<00:00, 17.72it/s]"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = weisfeilerlehmankernel\n",
    "param_grid_precomputed = {'height': np.linspace(0, 10, 11), 'base_kernel': ['edge']}\n",
    "param_grid = {'alpha': np.logspace(-2, 2, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 13.253133296966553 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 0.0} is: \n",
      "[[ 3.  1.  3. ...  1.  1.  1.]\n",
      " [ 1.  6.  1. ...  0.  0.  3.]\n",
      " [ 3.  1.  3. ...  1.  1.  1.]\n",
      " ...\n",
      " [ 1.  0.  1. ... 55. 21.  7.]\n",
      " [ 1.  0.  1. ... 21. 55.  7.]\n",
      " [ 1.  3.  1. ...  7.  7. 55.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9497f6e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 26.24978256225586 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 1.0} is: \n",
      "[[  6.   2.   6. ...   2.   2.   2.]\n",
      " [  2.  12.   2. ...   0.   0.   6.]\n",
      " [  6.   2.   6. ...   2.   2.   2.]\n",
      " ...\n",
      " [  2.   0.   2. ... 110.  42.  14.]\n",
      " [  2.   0.   2. ...  42. 110.  14.]\n",
      " [  2.   6.   2. ...  14.  14. 110.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9497e5a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 39.78876876831055 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 2.0} is: \n",
      "[[  9.   3.   9. ...   3.   3.   3.]\n",
      " [  3.  18.   3. ...   0.   0.   9.]\n",
      " [  9.   3.   9. ...   3.   3.   3.]\n",
      " ...\n",
      " [  3.   0.   3. ... 165.  63.  21.]\n",
      " [  3.   0.   3. ...  63. 165.  21.]\n",
      " [  3.   9.   3. ...  21.  21. 165.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495d37438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 52.122785806655884 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 3.0} is: \n",
      "[[ 12.   4.  12. ...   4.   4.   4.]\n",
      " [  4.  24.   4. ...   0.   0.  12.]\n",
      " [ 12.   4.  12. ...   4.   4.   4.]\n",
      " ...\n",
      " [  4.   0.   4. ... 220.  84.  28.]\n",
      " [  4.   0.   4. ...  84. 220.  28.]\n",
      " [  4.  12.   4. ...  28.  28. 220.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495c27b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 64.37597727775574 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 4.0} is: \n",
      "[[ 15.   5.  15. ...   5.   5.   5.]\n",
      " [  5.  30.   5. ...   0.   0.  15.]\n",
      " [ 15.   5.  15. ...   5.   5.   5.]\n",
      " ...\n",
      " [  5.   0.   5. ... 275. 105.  35.]\n",
      " [  5.   0.   5. ... 105. 275.  35.]\n",
      " [  5.  15.   5. ...  35.  35. 275.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495b1f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 79.43934679031372 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 5.0} is: \n",
      "[[ 18.   6.  18. ...   6.   6.   6.]\n",
      " [  6.  36.   6. ...   0.   0.  18.]\n",
      " [ 18.   6.  18. ...   6.   6.   6.]\n",
      " ...\n",
      " [  6.   0.   6. ... 330. 126.  42.]\n",
      " [  6.   0.   6. ... 126. 330.  42.]\n",
      " [  6.  18.   6. ...  42.  42. 330.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495ad96a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 91.50003838539124 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 6.0} is: \n",
      "[[ 21.   7.  21. ...   7.   7.   7.]\n",
      " [  7.  42.   7. ...   0.   0.  21.]\n",
      " [ 21.   7.  21. ...   7.   7.   7.]\n",
      " ...\n",
      " [  7.   0.   7. ... 385. 147.  49.]\n",
      " [  7.   0.   7. ... 147. 385.  49.]\n",
      " [  7.  21.   7. ...  49.  49. 385.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495a172e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 103.36761713027954 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 7.0} is: \n",
      "[[ 24.   8.  24. ...   8.   8.   8.]\n",
      " [  8.  48.   8. ...   0.   0.  24.]\n",
      " [ 24.   8.  24. ...   8.   8.   8.]\n",
      " ...\n",
      " [  8.   0.   8. ... 440. 168.  56.]\n",
      " [  8.   0.   8. ... 168. 440.  56.]\n",
      " [  8.  24.   8. ...  56.  56. 440.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f949596eeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 116.12333679199219 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 8.0} is: \n",
      "[[ 27.   9.  27. ...   9.   9.   9.]\n",
      " [  9.  54.   9. ...   0.   0.  27.]\n",
      " [ 27.   9.  27. ...   9.   9.   9.]\n",
      " ...\n",
      " [  9.   0.   9. ... 495. 189.  63.]\n",
      " [  9.   0.   9. ... 189. 495.  63.]\n",
      " [  9.  27.   9. ...  63.  63. 495.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495ace780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 129.00585055351257 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 9.0} is: \n",
      "[[ 30.  10.  30. ...  10.  10.  10.]\n",
      " [ 10.  60.  10. ...   0.   0.  30.]\n",
      " [ 30.  10.  30. ...  10.  10.  10.]\n",
      " ...\n",
      " [ 10.   0.  10. ... 550. 210.  70.]\n",
      " [ 10.   0.  10. ... 210. 550.  70.]\n",
      " [ 10.  30.  10. ...  70.  70. 550.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9497e5ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 183 built in 142.7631003856659 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'sp', 'height': 10.0} is: \n",
      "[[ 33.  11.  33. ...  11.  11.  11.]\n",
      " [ 11.  66.  11. ...   0.   0.  33.]\n",
      " [ 33.  11.  33. ...  11.  11.  11.]\n",
      " ...\n",
      " [ 11.   0.  11. ... 605. 231.  77.]\n",
      " [ 11.   0.  11. ... 231. 605.  77.]\n",
      " [ 11.  33.  11. ...  77.  77. 605.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9495b1ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 2/13530 [00:00<18:41, 12.07it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                            \n",
      "4. Getting final performances...\n",
      "\n",
      "best_params_out:  [{'base_kernel': 'sp', 'height': 2.0}]\n",
      "best_params_in:  [{'alpha': 100.0}]\n",
      "best_val_perf:  35.99396330118595\n",
      "best_val_std:  0.975168619010553\n",
      "final_performance:  37.92011979777975\n",
      "final_confidence:  5.599617973456408\n",
      "train_performance:  28.31827397760314\n",
      "train_std:  0.6337452324001842\n",
      "time to calculate gram matrix:  39.78876876831055 s\n",
      "\n",
      "params                                                      train_perf            valid_perf             test_perf               gram_matrix_time\n",
      "----------------------------------------------------------  --------------------  ---------------------  --------------------  ------------------\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 0.0}   6303.33±17673.66      12355.82±26031.70      23725.39±87669.55                  13.25\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 1.0}   11167.76±22193.97     41656.88±103467.38     32381.60±72912.93                  26.25\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 2.0}   14795.24±25795.77     35568.25±55185.30      29590.89±50716.39                  39.79\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 3.0}   17333.71±39530.48     47391.61±108495.33     44509.59±98871.73                  52.12\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 4.0}   18278.98±33231.47     41847.75±72770.75      34135.41±55211.24                  64.38\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 5.0}   29803.79±66494.69     78569.33±175795.65     82370.17±157243.38                 79.44\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 6.0}   17632.65±22083.28     39884.05±38376.83      50945.19±82473.15                  91.5\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 7.0}   47674.56±138824.33    103908.47±267675.59    149730.54±504033.41               103.37\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 8.0}   38700.43±57742.33     100584.20±172213.12    125033.09±243352.04               116.12\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 9.0}   51137.13±109807.16    158062.18±287987.15    111850.92±189317.22               129.01\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'sp', 'height': 10.0}  91585.56±358714.20    204806.70±740337.04    171099.59±539990.26               142.76\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 0.0}   3768.93±6563.97       8039.24±10612.57       12197.40±30860.60                  13.25\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 1.0}   8534.90±16639.10      31484.39±77224.48      24146.92±51302.34                  26.25\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 2.0}   9522.71±13808.72      23859.97±34172.83      20790.22±36158.94                  39.79\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 3.0}   15137.34±34822.01     41325.26±96108.07      38166.76±85847.44                  52.12\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 4.0}   14602.91±26904.97     33388.42±58724.36      27246.28±44445.54                  64.38\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 5.0}   36376.37±92923.37     89270.52±213430.12     154091.39±564983.92                79.44\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 6.0}   13950.65±17415.09     31638.00±30110.29      40270.40±65177.46                  91.5\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 7.0}   38475.48±113598.06    83787.88±218535.51     121394.03±413538.68               103.37\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 8.0}   31296.81±45734.25     80949.11±137088.59     101612.85±192522.79               116.12\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 9.0}   40632.06±86164.37     125455.67±226493.68    89065.57±149368.33                129.01\n",
      "{'alpha': '1.26e-02', 'base_kernel': 'sp', 'height': 10.0}  588900.37±3110944.10  1208690.68±6314266.44  912325.54±4674254.12              142.76\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 0.0}   3402.21±4861.55       6927.14±7628.84        9974.47±17914.08                   13.25\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 1.0}   6617.26±12608.36      24122.00±58386.26      18238.60±36649.01                  26.25\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 2.0}   7174.76±10260.62      18390.19±26551.90      16156.03±28603.75                  39.79\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 3.0}   15445.15±35597.93     42187.40±100161.18     36849.70±81732.90                  52.12\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 4.0}   11719.06±21952.36     26721.73±47654.14      21827.47±35996.70                  64.38\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 5.0}   19558.92±45231.30     51820.00±121029.46     53575.36±104092.62                 79.44\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 6.0}   11054.71±13722.93     25122.73±23593.67      31880.38±51465.74                  91.5\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 7.0}   31243.81±93849.12     67929.02±179990.76     99115.10±342685.77                103.37\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 8.0}   24475.48±36661.26     63742.72±109997.86     78962.46±153441.71                116.12\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 9.0}   32338.18±67471.74     99667.67±177830.20     71074.49±117877.28                129.01\n",
      "{'alpha': '1.58e-02', 'base_kernel': 'sp', 'height': 10.0}  44661.93±154596.34    102722.00±322326.26    88277.53±235092.65                142.76\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 0.0}   2323.26±3304.60       5144.94±5951.59        6681.02±11271.13                   13.25\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 1.0}   5297.66±9767.34       19017.71±44815.21      14128.45±26709.36                  26.25\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 2.0}   6100.66±8418.77       16665.03±24140.44      14252.41±24053.28                  39.79\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 3.0}   36896.38±113586.18    80947.13±190128.91     84225.84±237100.17                 52.12\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 4.0}   9473.74±18126.17      21491.97±38982.70      17590.16±29442.06                  64.38\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 5.0}   15381.25±37822.87     41288.25±102502.73     38927.96±74614.60                  79.44\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 6.0}   8768.30±10806.66      19968.19±18466.73      25256.94±40607.09                  91.5\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 7.0}   25597.38±78568.16     55496.02±150071.14     81734.72±287878.47                103.37\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 8.0}   19449.04±29299.35     50750.84±88218.43      62647.17±121963.73                116.12\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 9.0}   25681.00±52825.34     79181.94±139558.55     56756.57±93381.45                 129.01\n",
      "{'alpha': '2.00e-02', 'base_kernel': 'sp', 'height': 10.0}  23431.11±57792.85     57201.81±124925.25     52031.72±94962.04                 142.76\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 0.0}   1706.68±1997.53       4156.92±5066.65        4811.88±7065.20                    13.25\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 1.0}   5737.08±12922.22      19314.99±45491.91      13605.41±26445.29                  26.25\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 2.0}   5554.08±8376.77       13323.20±18835.42      11745.90±19671.83                  39.79\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 3.0}   10999.47±34932.16     29963.67±97524.08      27407.75±85255.47                  52.12\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 4.0}   7756.89±15272.71      17431.87±32281.45      14323.33±24509.35                  64.38\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 5.0}   12646.03±32780.60     34234.15±89891.06      30903.80±61870.70                  79.44\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 6.0}   6962.54±8506.63       15891.34±14442.07      20025.21±32016.11                  91.5\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 7.0}   21249.12±67027.30     45854.18±127344.48     68392.68±246542.65                103.37\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 8.0}   15482.19±23467.38     40480.24±70964.55      49789.15±96986.21                 116.12\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 9.0}   20569.62±41430.11     63260.95±109672.08     45739.85±74687.08                 129.01\n",
      "{'alpha': '2.51e-02', 'base_kernel': 'sp', 'height': 10.0}  15344.15±28899.03     38829.90±65159.35      36443.09±54440.50                 142.76\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 0.0}   1812.34±3328.07       4517.74±7699.99        5334.46±11850.11                   13.25\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 1.0}   3324.62±5881.84       11714.75±26863.60      8516.88±14830.75                   26.25\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 2.0}   3883.95±5846.81       9900.98±14890.77       8853.41±15625.79                   39.79\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 3.0}   16572.68±72812.56     46109.58±204728.63     40841.93±176065.17                 52.12\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 4.0}   6522.78±13415.44      14395.81±27297.15      11930.62±21194.20                  64.38\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 5.0}   10752.24±29757.31     29386.75±82846.79      25518.23±54417.30                  79.44\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 6.0}   5537.57±6695.23       12669.69±11289.46      15895.51±25225.36                  91.5\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 7.0}   18006.72±58796.31     38560.98±110938.86     58539.30±217197.03                103.37\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 8.0}   12345.72±18866.15     32353.91±57339.24      39626.99±77215.49                 116.12\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 9.0}   16755.53±33025.17     51204.59±87297.18      37580.82±61861.63                 129.01\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'sp', 'height': 10.0}  10917.07±16757.44     28282.57±39373.53      27063.87±36881.79                 142.76\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 0.0}   1701.12±3189.69       6228.13±19072.20       3746.43±5345.81                    13.25\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 1.0}   2481.43±4218.54       8721.55±20347.10       6492.29±11068.31                   26.25\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 2.0}   3072.55±4791.99       7921.48±12602.09       6942.43±12747.11                   39.79\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 3.0}   11604.23±49720.92     32322.99±140230.45     28780.68±120751.41                 52.12\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 4.0}   5923.37±13488.67      12371.10±24456.66      10555.83±20831.26                  64.38\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 5.0}   9684.83±29484.54      26833.56±83789.06      22147.64±52139.65                  79.44\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 6.0}   4415.64±5270.17       10128.04±8823.85       12641.33±19861.62                  91.5\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 7.0}   15852.07±53873.47     33478.64±100769.54     52268.31±199990.98                103.37\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 8.0}   9869.84±15253.60      25935.78±46628.64      31608.74±61607.54                 116.12\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 9.0}   14477.22±29009.68     43449.52±75315.39      32950.38±59664.94                 129.01\n",
      "{'alpha': '3.98e-02', 'base_kernel': 'sp', 'height': 10.0}  8096.84±10755.21      21315.51±26117.25      20661.11±27131.39                 142.76\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 0.0}   834.25±897.42         2250.77±3160.40        2286.24±2994.25                    13.25\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 1.0}   4772.85±15849.74      10935.54±26327.58      8064.85±16783.61                   26.25\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 2.0}   2872.84±4670.43       7070.37±11673.13       6309.78±11443.46                   39.79\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 3.0}   4308.50±12657.79      11767.04±35381.85      10993.55±31407.59                  52.12\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 4.0}   151764.66±812957.25   151573.89±784519.35    213676.11±1136693.41               64.38\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 5.0}   10207.91±36268.27     28896.04±106100.30     21814.45±61722.17                  79.44\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 6.0}   3533.92±4150.86       8126.65±6897.43        10082.44±15628.75                  91.5\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 7.0}   14814.28±53186.63     30605.36±98450.06      49368.78±198322.84                103.37\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 8.0}   7923.49±12441.66      20888.65±38281.24      25315.06±49342.79                 116.12\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 9.0}   23740.36±83454.02     64160.95±202799.90     56787.26±205644.99                129.01\n",
      "{'alpha': '5.01e-02', 'base_kernel': 'sp', 'height': 10.0}  6156.18±7429.06       16387.77±18431.68      16025.72±20754.54                 142.76\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 0.0}   680.06±759.87         1755.31±2249.35        1800.37±2297.44                    13.25\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 1.0}   1487.10±2403.34       5367.92±12309.78       4060.60±6641.01                    26.25\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 2.0}   2569.92±4858.09       6501.50±12447.45       5400.45±10867.48                   39.79\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 3.0}   2836.84±6408.56       7476.78±16804.80       7183.84±15696.99                   52.12\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 4.0}   3969.37±9296.64       8512.05±18714.80       7020.04±13965.44                   64.38\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 5.0}   33967.75±169622.50    102667.58±518103.72    60392.94±280631.92                 79.44\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 6.0}   2857.77±3264.58       6573.93±5378.61        8100.03±12278.90                   91.5\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 7.0}   15222.97±61120.27     30754.07±111848.55     52158.17±228814.06                103.37\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 8.0}   6414.19±10295.29      16979.06±31893.97      20502.03±39814.65                 116.12\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 9.0}   9237.24±18448.57      27528.23±47723.63      21308.15±40081.82                 129.01\n",
      "{'alpha': '6.31e-02', 'base_kernel': 'sp', 'height': 10.0}  4767.49±5394.43       12780.17±13539.03      12573.28±16164.22                 142.76\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 0.0}   1151.05±3339.29       3238.73±10857.56       2721.97±7584.68                    13.25\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 1.0}   1238.91±1878.00       4265.39±9676.41        3261.55±5025.86                    26.25\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 2.0}   6623.55±20063.54      14202.25±41137.00      12097.70±38056.93                  39.79\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 3.0}   3606.28±9332.05       9808.70±22314.74       8154.59±17904.99                   52.12\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 4.0}   3283.98±7983.29       7416.05±18008.67       5838.94±11564.90                   64.38\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 5.0}   7428.02±25896.16      21389.21±75522.01      16306.25±45559.21                  79.44\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 6.0}   2332.59±2573.99       5362.62±4201.36        6556.87±9643.76                    91.5\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 7.0}   23856.19±111470.61    45837.78±201957.39     85222.77±418320.71                103.37\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 8.0}   5234.47±8701.12       13904.46±27197.46      16642.70±32501.96                 116.12\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 9.0}   6127.60±10443.05      18842.57±28929.22      13959.59±20242.76                 129.01\n",
      "{'alpha': '7.94e-02', 'base_kernel': 'sp', 'height': 10.0}  4161.08±4421.73       10774.68±10392.46      10587.68±12776.94                 142.76\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 0.0}   545.03±706.84         1214.04±1367.67        1305.35±1469.96                    13.25\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 1.0}   1231.30±1751.27       3927.93±7932.28        3529.36±5777.73                    26.25\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 2.0}   2363.84±4738.11       5844.33±12039.10       4756.98±9194.94                    39.79\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 3.0}   1897.24±4527.19       4709.00±9718.09        4501.55±9545.93                    52.12\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 4.0}   3976.80±10959.83      8485.22±23090.27       7164.33±18149.02                   64.38\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 5.0}   4703.35±15005.54      12362.04±38906.71      10531.94±25834.10                  79.44\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 6.0}   1975.71±2062.28       4485.73±3319.66        5433.45±7573.52                    91.5\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 7.0}   29597.78±143503.80    55853.17±259376.30     106935.97±538451.64               103.37\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 8.0}   4380.00±7645.98       11663.78±24161.98      13828.87±27217.05                 116.12\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 9.0}   4549.23±7324.03       14097.75±20993.30      10400.14±14095.80                 129.01\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'sp', 'height': 10.0}  3076.05±3196.16       8146.84±7932.14        8019.92±10035.56                  142.76\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 0.0}   393.24±333.57         912.35±712.52          1039.25±1033.27                    13.25\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 1.0}   1287.33±2882.76       3566.75±7560.31        4372.56±13718.50                   26.25\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 2.0}   1897.63±3127.60       4730.92±8235.63        3948.78±7398.67                    39.79\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 3.0}   1250.87±2129.08       3122.28±4959.93        3041.04±4903.40                    52.12\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 4.0}   6176.06±24487.49      14997.30±61326.15      8789.78±27176.70                   64.38\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 5.0}   3046.99±7195.74       7861.87±18150.06       7941.93±16088.13                   79.44\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 6.0}   1651.97±1657.99       3809.79±2637.45        4790.57±6404.92                    91.5\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 7.0}   8266.61±27577.07      16428.14±50783.59      26937.19±102360.43                103.37\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 8.0}   3830.28±7315.81       10262.57±23368.85      11935.69±24393.92                 116.12\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 9.0}   3333.40±5254.34       10501.99±15544.38      7724.93±10372.21                  129.01\n",
      "{'alpha': '1.26e-01', 'base_kernel': 'sp', 'height': 10.0}  2355.02±2432.24       6381.11±6132.07        6323.10±7947.14                   142.76\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 0.0}   987.04±3430.40        1516.44±3537.79        2048.54±5011.98                    13.25\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 1.0}   893.86±1553.46        3118.86±7272.18        1820.70±2573.37                    26.25\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 2.0}   1051.49±1740.21       2783.26±4592.68        2411.64±5213.85                    39.79\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 3.0}   1665.73±4449.33       3264.69±6892.97        3266.88±6275.25                    52.12\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 4.0}   3070.96±8336.51       7162.22±20478.54       4896.61±10069.33                   64.38\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 5.0}   3189.83±6524.09       7203.79±14451.87       8017.59±17308.73                   79.44\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 6.0}   1460.34±1423.49       3369.50±2300.81        3920.74±4811.40                    91.5\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 7.0}   11268.54±39883.22     20562.41±65502.43      38277.74±143351.07                103.37\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 8.0}   3906.02±8899.77       10575.85±28554.60      11626.77±27381.16                 116.12\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 9.0}   2497.03±3763.45       7962.29±11508.02       5841.11±7726.10                   129.01\n",
      "{'alpha': '1.58e-01', 'base_kernel': 'sp', 'height': 10.0}  1880.34±1909.89       5135.89±4830.74        5095.65±6298.92                   142.76\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 0.0}   335.30±230.75         875.21±713.55          1085.66±1258.03                    13.25\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 1.0}   682.01±1000.38        2539.20±6516.93        1521.32±2323.02                    26.25\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 2.0}   2087.48±5001.27       5452.53±12389.15       4343.69±12599.47                   39.79\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 3.0}   676.52±834.45         1698.59±1948.51        1783.36±2133.86                    52.12\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 4.0}   2766.45±8367.13       6991.50±21983.27       5034.32±13513.49                   64.38\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 5.0}   11392.47±40527.42     26808.27±96896.63      34824.45±150399.43                 79.44\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 6.0}   1260.68±1247.14       3187.49±3321.68        3223.18±3846.98                    91.5\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 7.0}   3285.77±8714.53       7824.52±21337.93       9355.77±26538.69                  103.37\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 8.0}   71420.63±381144.56    228140.64±1225336.89   224226.44±1196214.95              116.12\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 9.0}   1903.23±2699.02       6180.65±8548.05        4524.93±5802.60                   129.01\n",
      "{'alpha': '2.00e-01', 'base_kernel': 'sp', 'height': 10.0}  1580.34±1531.54       4301.87±3935.30        4276.73±5016.39                   142.76\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 0.0}   401.28±551.55         1074.26±1357.91        1222.40±1836.76                    13.25\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 1.0}   786.57±1749.17        4063.58±15466.70       1710.58±3986.79                    26.25\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 2.0}   743.06±921.48         2122.22±2961.46        1735.42±2927.44                    39.79\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 3.0}   455.09±459.54         1235.28±1321.72        1317.90±1498.39                    52.12\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 4.0}   3692.45±13938.33      9522.33±35805.14       9930.95±41966.12                   64.38\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 5.0}   1754.93±3148.56       4777.58±8581.38        5257.31±11185.79                   79.44\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 6.0}   1214.51±1398.61       2778.92±2496.59        3000.85±3559.93                    91.5\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 7.0}   5157.46±19346.98      13889.96±57369.88      10732.12±33959.35                 103.37\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 8.0}   2609.55±5660.76       7020.31±17624.58       7594.56±15875.30                  116.12\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 9.0}   2063.15±3955.45       7484.87±17049.49       4831.87±8613.82                   129.01\n",
      "{'alpha': '2.51e-01', 'base_kernel': 'sp', 'height': 10.0}  1280.19±1230.03       3533.78±3206.22        3480.61±4002.11                   142.76\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 0.0}   378.76±555.83         1023.31±1600.95        1126.33±1701.85                    13.25\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 1.0}   2475.25±10482.86      5477.28±15819.43       4944.52±20510.60                   26.25\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 2.0}   751.11±771.76         2054.06±2419.31        1795.60±2507.90                    39.79\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 3.0}   368.00±315.85         1003.89±968.71         1098.56±1132.48                    52.12\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 4.0}   957.58±1151.50        2780.80±4252.06        2066.49±2722.42                    64.38\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 5.0}   1061.65±1427.32       2947.49±4678.79        2782.41±3951.64                    79.44\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 6.0}   1978.17±4768.70       3699.81±7455.57        4112.44±8217.01                    91.5\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 7.0}   1973.55±5484.54       5234.82±15804.86       4708.13±11198.72                  103.37\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 8.0}   3363.14±8745.17       8611.23±25070.58       7569.05±16587.39                  116.12\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 9.0}   1153.35±1450.27       3658.42±4645.51        2783.43±3397.71                   129.01\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'sp', 'height': 10.0}  1050.51±1010.76       2930.57±2704.96        2854.07±3214.79                   142.76\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 0.0}   275.58±242.80         694.98±854.81          870.79±947.55                      13.25\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 1.0}   804.89±2589.58        1578.37±3723.52        1510.52±3815.99                    26.25\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 2.0}   995.86±2651.98        3059.54±7733.15        3278.93±11066.34                   39.79\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 3.0}   1377.25±5551.01       2591.96±9189.62        4589.48±19134.09                   52.12\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 4.0}   521.19±559.18         1340.78±1326.65        1151.23±1218.05                    64.38\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 5.0}   975.87±1951.32        2346.85±3715.26        2590.07±5122.07                    79.44\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 6.0}   1178.06±2068.18       2354.25±3263.94        2665.16±4075.85                    91.5\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 7.0}   1107.44±1994.18       2943.63±5462.83        2835.50±5166.80                   103.37\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 8.0}   1607.54±2843.13       4337.88±8910.01        4984.27±9024.38                   116.12\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 9.0}   884.36±995.74         2821.99±3353.17        2237.22±2527.26                   129.01\n",
      "{'alpha': '3.98e-01', 'base_kernel': 'sp', 'height': 10.0}  923.09±886.50         2647.56±2560.28        2522.59±2691.83                   142.76\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 0.0}   228.58±212.42         528.85±396.79          724.90±824.41                      13.25\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 1.0}   278.11±313.42         852.43±1191.77         625.82±668.67                      26.25\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 2.0}   825.79±1319.94        3366.72±7097.38        2779.87±6170.34                    39.79\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 3.0}   837.27±2750.02        2212.01±7619.98        2647.12±9278.17                    52.12\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 4.0}   508.25±475.56         1291.61±1184.71        1170.82±1130.50                    64.38\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 5.0}   651.10±692.95         1822.69±2416.04        1619.55±1566.94                    79.44\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 6.0}   1454.95±4411.46       2706.75±6804.71        2798.44±5952.56                    91.5\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 7.0}   798.70±1493.42        2184.30±4594.16        2061.02±3558.94                   103.37\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 8.0}   1210.21±2464.67       2909.98±6211.39        3104.43±5488.84                   116.12\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 9.0}   738.96±747.92         2381.85±2555.59        1802.01±1942.08                   129.01\n",
      "{'alpha': '5.01e-01', 'base_kernel': 'sp', 'height': 10.0}  882.14±1209.41        2587.12±4144.76        2243.90±2851.77                   142.76\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 0.0}   171.34±121.82         412.56±321.12          510.62±400.92                      13.25\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 1.0}   344.76±479.07         959.71±1415.03         699.51±728.27                      26.25\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 2.0}   436.60±554.63         1437.93±2353.49        1356.78±2652.49                    39.79\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 3.0}   293.62±243.54         851.22±935.67          856.78±885.31                      52.12\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 4.0}   568.28±1146.87        1382.18±2741.51        1167.30±1975.91                    64.38\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 5.0}   608.96±816.93         1390.46±1236.51        1423.62±1484.30                    79.44\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 6.0}   957.60±1096.55        1936.60±1861.27        2404.79±2913.43                    91.5\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 7.0}   703.76±1319.68        1960.75±4325.08        1774.14±2866.72                   103.37\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 8.0}   850.36±1364.27        2608.20±6411.59        2310.74±3752.09                   116.12\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 9.0}   559.80±549.07         1769.53±1823.78        1405.03±1501.48                   129.01\n",
      "{'alpha': '6.31e-01', 'base_kernel': 'sp', 'height': 10.0}  653.89±729.10         1870.07±2360.89        1674.93±1935.89                   142.76\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 0.0}   172.53±191.94         479.84±750.45          564.91±736.81                      13.25\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 1.0}   4165.18±15118.91      10454.60±40480.81      9184.73±36041.84                   26.25\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 2.0}   409.22±482.30         1211.77±1415.81        1008.60±1614.57                    39.79\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 3.0}   263.50±239.66         719.59±684.34          718.15±697.62                      52.12\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 4.0}   519.54±724.84         1176.37±1284.97        1217.81±1650.90                    64.38\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 5.0}   526.88±792.23         1516.67±2700.48        1414.25±2274.82                    79.44\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 6.0}   595.38±737.37         1388.87±1559.55        1571.58±2075.53                    91.5\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 7.0}   550.27±814.84         1468.74±1991.86        1318.78±1654.08                   103.37\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 8.0}   971.09±2000.96        2252.21±4162.18        2297.08±3565.60                   116.12\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 9.0}   575.20±582.60         1776.09±1806.91        1348.24±1333.22                   129.01\n",
      "{'alpha': '7.94e-01', 'base_kernel': 'sp', 'height': 10.0}  514.05±400.30         1389.84±1081.17        1365.91±1305.43                   142.76\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 0.0}   313.16±1027.13        1194.78±4732.55        1335.90±5057.96                    13.25\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 1.0}   146.91±87.68          423.68±306.58          359.56±254.83                      26.25\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 2.0}   397.32±579.51         1504.03±3202.65        853.80±1207.73                     39.79\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 3.0}   233.27±337.69         611.06±824.46          694.81±1045.53                     52.12\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 4.0}   413.31±724.29         904.27±1430.04         899.51±1278.97                     64.38\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 5.0}   404.15±512.47         1173.24±1461.10        999.04±1214.87                     79.44\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 6.0}   10178.23±49845.32     17050.42±82306.41      21545.71±104210.20                 91.5\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 7.0}   439.31±481.84         1187.98±1456.07        1077.96±1102.43                   103.37\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 8.0}   557.17±686.54         1360.44±1724.16        1764.80±2757.79                   116.12\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 9.0}   487.38±587.53         1450.71±1733.51        1193.33±1366.25                   129.01\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'sp', 'height': 10.0}  578.58±665.47         1430.03±1373.97        1652.52±2404.96                   142.76\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 0.0}   115.80±142.08         316.05±426.36          312.55±291.13                      13.25\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 1.0}   261.99±586.53         708.50±1360.62         642.57±1469.16                     26.25\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 2.0}   410.80±1096.22        1252.51±3603.69        767.98±1615.51                     39.79\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 3.0}   175.96±137.65         453.90±442.06          516.49±457.84                      52.12\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 4.0}   286.79±492.92         679.84±889.89          681.03±1108.27                     64.38\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 5.0}   4940.06±25374.82      29209.38±155949.19     29379.75±156911.70                 79.44\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 6.0}   388.67±446.70         932.14±1156.17         984.49±1099.88                     91.5\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 7.0}   330.59±285.59         855.55±766.79          866.81±738.97                     103.37\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 8.0}   463.48±498.55         1062.10±1143.48        1491.70±2247.83                   116.12\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 9.0}   527.66±963.72         1398.08±2065.03        1124.32±1356.33                   129.01\n",
      "{'alpha': '1.26e+00', 'base_kernel': 'sp', 'height': 10.0}  549.31±766.58         1483.15±2662.34        1414.78±2543.72                   142.76\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 0.0}   101.29±105.17         222.84±136.67          304.77±327.87                      13.25\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 1.0}   132.58±136.61         368.01±329.84          363.26±510.95                      26.25\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 2.0}   263.15±356.73         728.88±849.57          650.41±1094.24                     39.79\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 3.0}   189.44±179.49         565.26±751.93          666.38±883.03                      52.12\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 4.0}   243.65±343.37         555.44±569.30          616.19±968.13                      64.38\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 5.0}   280.58±411.24         740.05±887.62          742.31±923.07                      79.44\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 6.0}   358.92±580.34         1192.99±3178.66        923.14±1274.35                     91.5\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 7.0}   581.48±1685.57        1660.38±5064.65        1177.61±2276.05                   103.37\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 8.0}   588.85±1516.93        1186.02±2056.83        2189.04±6045.15                   116.12\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 9.0}   340.37±296.05         953.18±703.55          870.74±827.91                     129.01\n",
      "{'alpha': '1.58e+00', 'base_kernel': 'sp', 'height': 10.0}  393.93±508.78         1175.08±1840.76        949.81±1045.93                    142.76\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 0.0}   82.18±85.16           192.93±148.82          268.46±443.72                      13.25\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 1.0}   206.26±358.79         610.55±1026.92         525.28±1196.95                     26.25\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 2.0}   252.28±659.67         856.23±2475.25         544.08±1399.20                     39.79\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 3.0}   219.02±316.64         548.68±715.50          829.26±1867.39                     52.12\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 4.0}   552.28±2174.31        1106.39±3821.30        1431.52±5848.64                    64.38\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 5.0}   396.62±1220.71        1944.65±7950.58        1496.46±5415.17                    79.44\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 6.0}   210.91±143.24         528.43±366.88          616.07±616.92                      91.5\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 7.0}   255.69±158.81         637.67±437.23          748.84±681.84                     103.37\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 8.0}   449.03±992.84         1298.26±3237.24        2287.11±8474.64                   116.12\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 9.0}   370.83±512.01         942.76±877.80          957.55±1443.97                    129.01\n",
      "{'alpha': '2.00e+00', 'base_kernel': 'sp', 'height': 10.0}  276.02±190.90         730.36±504.35          685.34±565.17                     142.76\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 0.0}   60.66±69.56           159.65±249.39          158.56±189.06                      13.25\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 1.0}   89.37±66.89           248.67±245.08          209.53±190.11                      26.25\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 2.0}   149.06±227.83         457.05±737.33          328.97±529.72                      39.79\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 3.0}   198.54±281.62         592.82±942.47          677.67±1116.40                     52.12\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 4.0}   144.68±107.98         400.86±326.76          345.16±263.09                      64.38\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 5.0}   214.80±300.35         572.89±797.00          734.57±1347.83                     79.44\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 6.0}   377.36±588.85         771.73±910.77          1074.25±1643.66                    91.5\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 7.0}   540.84±1424.94        1593.60±4133.64        1602.14±4130.62                   103.37\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 8.0}   431.32±899.82         1745.71±5617.21        1289.64±2184.99                   116.12\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 9.0}   477.79±829.83         1348.11±2152.20        1189.19±1817.65                   129.01\n",
      "{'alpha': '2.51e+00', 'base_kernel': 'sp', 'height': 10.0}  1847.52±8802.93       3593.44±16241.98       2425.17±9855.65                   142.76\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 0.0}   62.88±72.72           178.51±235.00          193.17±338.99                      13.25\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 1.0}   67.62±60.19           180.59±164.36          175.16±232.81                      26.25\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 2.0}   209.46±566.22         572.08±1394.43         617.18±2030.38                     39.79\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 3.0}   290.00±952.86         1149.72±4715.08        949.83±2923.23                     52.12\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 4.0}   355.90±1207.42        615.93±1287.54         860.16±2928.02                     64.38\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 5.0}   502.74±1767.21        1719.42±6588.93        2185.76±9234.25                    79.44\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 6.0}   454.85±848.72         1040.30±1325.28        1382.82±2697.86                    91.5\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 7.0}   7351.65±38531.25      21237.36±111591.78     16151.87±83041.16                 103.37\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 8.0}   1496.99±6927.86       3169.09±13850.64       3761.52±16615.76                  116.12\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 9.0}   247.74±282.05         758.30±1009.60         601.89±565.05                     129.01\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'sp', 'height': 10.0}  291.72±345.44         871.92±1248.06         827.45±1220.86                    142.76\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 0.0}   37.14±17.31           77.65±36.91            80.58±64.66                        13.25\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 1.0}   52.82±27.07           141.48±85.95           129.16±148.44                      26.25\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 2.0}   95.57±176.82          350.39±863.65          193.30±298.35                      39.79\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 3.0}   80.10±38.77           223.56±160.92          236.68±142.92                      52.12\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 4.0}   235.63±566.69         567.39±1201.09         877.31±2704.85                     64.38\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 5.0}   134.60±113.24         406.24±430.90          415.14±495.27                      79.44\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 6.0}   341.09±561.21         926.04±1417.76         979.60±1522.69                     91.5\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 7.0}   622.53±1947.73        1659.66±5242.71        1519.89±3512.40                   103.37\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 8.0}   269.69±411.99         675.14±824.06          895.73±1945.44                    116.12\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 9.0}   250.37±471.20         584.43±542.18          568.28±832.90                     129.01\n",
      "{'alpha': '3.98e+00', 'base_kernel': 'sp', 'height': 10.0}  240.65±253.58         663.29±725.49          575.20±547.90                     142.76\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 0.0}   78.57±210.55          211.71±570.24          232.69±829.55                      13.25\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 1.0}   44.53±19.56           117.12±84.14           107.52±88.38                       26.25\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 2.0}   157.29±482.24         625.13±2269.89         328.50±1015.40                     39.79\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 3.0}   88.24±118.30          198.03±151.46          306.13±600.72                      52.12\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 4.0}   254.67±797.93         948.47±3560.27         547.50±1241.96                     64.38\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 5.0}   235.43±684.26         541.52±1415.03         681.29±2160.21                     79.44\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 6.0}   258.58±527.10         675.87±1120.47         800.92±1570.03                     91.5\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 7.0}   155.16±120.26         407.88±298.25          456.22±383.52                     103.37\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 8.0}   206.73±316.53         434.77±486.68          656.11±1142.99                    116.12\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 9.0}   177.39±191.56         553.77±609.05          535.66±1037.54                    129.01\n",
      "{'alpha': '5.01e+00', 'base_kernel': 'sp', 'height': 10.0}  167.24±139.02         457.57±426.91          467.86±494.62                     142.76\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 0.0}   53.64±129.99          323.52±1483.77         103.18±293.36                      13.25\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 1.0}   63.23±82.89           236.27±460.73          114.63±97.62                       26.25\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 2.0}   71.91±78.70           226.69±271.04          150.04±156.33                      39.79\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 3.0}   299.28±1210.64        576.75±2079.23         1156.72±4974.04                    52.12\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 4.0}   322.15±971.44         1081.00±3311.33        603.82±1431.22                     64.38\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 5.0}   104.20±73.96          319.58±297.05          257.07±170.52                      79.44\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 6.0}   311.99±623.55         979.18±2316.44         1146.52±2638.34                    91.5\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 7.0}   126.50±102.07         335.88±232.42          366.88±364.94                     103.37\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 8.0}   115.91±55.68          293.19±150.16          368.98±261.37                     116.12\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 9.0}   200.61±271.02         593.92±867.03          526.98±864.58                     129.01\n",
      "{'alpha': '6.31e+00', 'base_kernel': 'sp', 'height': 10.0}  176.73±295.53         476.23±712.28          548.79±1296.83                    142.76\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 0.0}   28.69±1.09            45.94±5.53             44.04±6.03                         13.25\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 1.0}   33.45±7.70            71.38±31.07            66.23±49.90                        26.25\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 2.0}   43.50±18.41           115.93±75.13           91.06±53.30                        39.79\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 3.0}   54.25±29.02           136.95±70.85           160.70±147.80                      52.12\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 4.0}   75.45±55.48           202.39±142.62          210.75±254.35                      64.38\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 5.0}   100.23±91.63          316.37±450.58          276.45±267.15                      79.44\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 6.0}   152.62±170.96         363.17±361.95          508.83±631.51                      91.5\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 7.0}   148.42±227.02         501.18±1227.73         379.72±427.84                     103.37\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 8.0}   364.11±1351.93        1071.36±4405.97        727.38±1868.73                    116.12\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 9.0}   157.27±191.02         608.72±1359.55         397.54±482.89                     129.01\n",
      "{'alpha': '7.94e+00', 'base_kernel': 'sp', 'height': 10.0}  159.21±291.95         380.87±419.23          416.37±636.28                     142.76\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 0.0}   28.20±0.71            42.43±2.31             41.26±5.17                         13.25\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 1.0}   30.44±1.91            57.35±12.58            51.51±11.90                        26.25\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 2.0}   60.93±102.00          181.18±420.19          181.20±456.02                      39.79\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 3.0}   123.08±347.61         321.00±1044.12         285.66±624.22                      52.12\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 4.0}   137.91±321.36         404.50±830.23          394.43±1232.55                     64.38\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 5.0}   105.08±159.05         225.72±195.95          323.31±572.33                      79.44\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 6.0}   86.52±41.99           236.26±166.44          267.77±162.53                      91.5\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 7.0}   120.03±114.73         362.36±424.83          349.10±433.82                     103.37\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 8.0}   152.92±234.95         391.20±620.26          509.08±976.63                     116.12\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 9.0}   153.03±230.19         426.80±550.86          364.67±518.27                     129.01\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'sp', 'height': 10.0}  107.77±95.05          342.81±422.94          304.25±329.30                     142.76\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 0.0}   27.95±0.60            40.31±1.52             39.41±4.90                         13.25\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 1.0}   29.43±1.84            51.28±10.24            46.44±6.93                         26.25\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 2.0}   31.61±3.44            63.43±14.82            60.12±20.85                        39.79\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 3.0}   60.94±113.69          167.50±378.67          170.70±411.22                      52.12\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 4.0}   61.57±96.04           190.80±466.61          138.18±190.08                      64.38\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 5.0}   74.13±93.13           176.84±183.20          198.88±311.89                      79.44\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 6.0}   72.48±45.37           166.21±87.94           237.52±238.07                      91.5\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 7.0}   206.45±562.39         913.51±3315.49         575.41±1282.68                    103.37\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 8.0}   216.95±641.80         624.98±2016.20         711.21±2255.48                    116.12\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 9.0}   87.27±56.06           290.68±293.16          204.39±124.69                     129.01\n",
      "{'alpha': '1.26e+01', 'base_kernel': 'sp', 'height': 10.0}  136.53±281.21         507.41±1508.84         328.13±628.50                     142.76\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 0.0}   27.85±0.54            38.78±1.18             38.02±4.90                         13.25\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 1.0}   28.50±1.09            45.14±2.88             43.01±4.86                         26.25\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 2.0}   30.98±7.34            59.86±38.29            55.06±17.57                        39.79\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 3.0}   41.08±25.77           93.44±94.36            104.43±131.55                      52.12\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 4.0}   55.83±65.16           203.93±508.77          151.93±260.27                      64.38\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 5.0}   43.16±19.01           105.97±75.54           100.59±75.24                       79.44\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 6.0}   68.72±73.07           183.61±231.20          192.09±205.52                      91.5\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 7.0}   93.36±146.60          231.62±422.95          327.68±662.23                     103.37\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 8.0}   76.91±78.82           163.99±98.55           270.13±421.43                     116.12\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 9.0}   85.42±85.15           294.52±498.28          212.57±198.81                     129.01\n",
      "{'alpha': '1.58e+01', 'base_kernel': 'sp', 'height': 10.0}  116.98±248.15         306.60±500.65          440.61±1412.19                    142.76\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 0.0}   27.89±0.51            37.66±1.00             36.96±5.06                         13.25\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 1.0}   28.09±0.94            42.25±1.73             40.95±4.27                         26.25\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 2.0}   28.72±1.40            47.28±4.82             47.96±8.01                         39.79\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 3.0}   34.17±9.39            67.25±34.37            74.33±62.51                        52.12\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 4.0}   46.28±60.79           125.88±313.09          94.99±149.78                       64.38\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 5.0}   44.46±32.72           116.55±152.75          100.50±122.67                      79.44\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 6.0}   69.12±104.19          204.42±330.98          170.65±204.70                      91.5\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 7.0}   58.27±60.50           128.02±87.38           173.15±284.61                     103.37\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 8.0}   81.93±145.79          163.82±153.51          281.76±700.83                     116.12\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 9.0}   94.06±166.74          284.39±675.16          236.87±400.97                     129.01\n",
      "{'alpha': '2.00e+01', 'base_kernel': 'sp', 'height': 10.0}  79.15±112.94          270.08±554.56          168.71±219.55                     142.76\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 0.0}   28.09±0.49            36.87±0.89             36.18±5.34                         13.25\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 1.0}   27.86±0.82            40.27±1.39             39.44±4.26                         26.25\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 2.0}   28.14±1.12            43.53±2.58             45.09±6.53                         39.79\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 3.0}   29.63±1.43            49.35±4.14             49.44±13.95                        52.12\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 4.0}   33.73±10.75           66.34±33.31            68.97±69.06                        64.38\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 5.0}   35.14±11.19           76.80±57.42            71.70±57.99                        79.44\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 6.0}   39.26±16.89           87.93±44.23            93.39±73.35                        91.5\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 7.0}   42.39±24.94           111.28±134.72          94.89±74.40                       103.37\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 8.0}   76.19±130.05          239.15±566.75          225.14±439.25                     116.12\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 9.0}   59.63±55.32           166.25±210.07          163.55±205.37                     129.01\n",
      "{'alpha': '2.51e+01', 'base_kernel': 'sp', 'height': 10.0}  85.71±198.31          350.56±1128.48         152.67±206.21                     142.76\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 0.0}   28.45±0.49            36.37±0.82             35.66±5.67                         13.25\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 1.0}   27.76±0.72            38.78±1.18             38.33±4.48                         26.25\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 2.0}   27.80±0.95            41.18±1.98             42.98±5.62                         39.79\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 3.0}   28.76±0.89            44.71±2.01             43.29±6.27                         52.12\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 4.0}   33.66±24.09           73.14±131.32           55.03±43.51                        64.38\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 5.0}   33.56±13.11           72.33±68.57            57.67±29.57                        79.44\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 6.0}   31.89±4.21            65.68±23.23            65.57±25.15                        91.5\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 7.0}   39.16±25.57           102.74±147.59          80.81±62.30                       103.37\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 8.0}   40.46±18.01           92.18±62.45            97.37±92.33                       116.12\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 9.0}   39.62±14.63           88.90±42.27            100.88±80.51                      129.01\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'sp', 'height': 10.0}  53.41±46.02           184.77±286.81          102.14±71.44                      142.76\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 0.0}   29.01±0.49            36.17±0.78             35.41±6.02                         13.25\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 1.0}   27.79±0.65            37.65±1.02             37.52±4.74                         26.25\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 2.0}   27.62±0.84            39.43±1.63             41.32±5.17                         39.79\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 3.0}   28.35±0.77            42.17±1.48             40.36±5.19                         52.12\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 4.0}   28.81±1.80            45.49±4.04             44.54±11.91                        64.38\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 5.0}   29.25±1.20            48.79±4.98             46.20±9.34                         79.44\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 6.0}   30.42±3.58            59.35±21.28            58.62±15.75                        91.5\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 7.0}   33.55±16.90           76.02±102.68           61.01±49.14                       103.37\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 8.0}   33.94±8.19            68.81±28.50            74.71±54.91                       116.12\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 9.0}   37.29±17.26           76.10±28.16            94.40±120.63                      129.01\n",
      "{'alpha': '3.98e+01', 'base_kernel': 'sp', 'height': 10.0}  37.06±21.20           74.59±34.49            90.76±119.74                      142.76\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 0.0}   29.89±0.71            36.41±0.99             35.55±6.45                         13.25\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 1.0}   27.98±0.59            36.84±0.90             36.99±4.99                         26.25\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 2.0}   27.57±0.76            38.10±1.39             40.01±5.06                         39.79\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 3.0}   28.11±0.68            40.31±1.15             38.36±4.85                         52.12\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 4.0}   28.16±0.95            42.18±1.66             40.60±6.32                         64.38\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 5.0}   28.58±0.98            44.34±2.32             42.82±7.47                         79.44\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 6.0}   28.41±1.16            46.42±3.22             49.37±8.40                         91.5\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 7.0}   29.38±2.13            50.52±10.65            48.54±11.72                       103.37\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 8.0}   34.48±21.28           58.91±31.31            74.47±102.29                      116.12\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 9.0}   39.64±38.29           73.46±60.57            111.30±231.17                     129.01\n",
      "{'alpha': '5.01e+01', 'base_kernel': 'sp', 'height': 10.0}  35.64±21.81           73.50±84.30            86.57±127.56                      142.76\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 0.0}   70.25±108.86          64.95±65.14            72.95±115.94                       13.25\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 1.0}   28.33±0.55            36.33±0.81             36.73±5.21                         26.25\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 2.0}   27.66±0.70            37.10±1.21             39.02±5.15                         39.79\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 3.0}   28.01±0.62            38.87±0.92             36.88±4.80                         52.12\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 4.0}   27.93±0.86            40.26±1.44             38.72±5.99                         64.38\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 5.0}   28.20±0.90            41.77±1.63             40.50±6.40                         79.44\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 6.0}   27.86±0.96            42.67±1.91             46.15±7.14                         91.5\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 7.0}   28.43±1.04            44.61±2.47             44.44±8.88                        103.37\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 8.0}   29.33±1.83            47.62±7.63             48.61±11.63                       116.12\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 9.0}   29.63±2.24            51.83±11.27            53.31±17.72                       129.01\n",
      "{'alpha': '6.31e+01', 'base_kernel': 'sp', 'height': 10.0}  30.27±4.31            53.46±17.47            54.80±26.98                       142.76\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 0.0}   35.95±2.71            39.97±1.51             38.80±8.91                         13.25\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 1.0}   28.88±0.52            36.10±0.74             36.72±5.40                         26.25\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 2.0}   27.90±0.66            36.41±1.08             38.33±5.35                         39.79\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 3.0}   28.04±0.58            37.77±0.77             35.78±4.92                         52.12\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 4.0}   27.83±0.78            38.83±1.29             37.33±5.89                         64.38\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 5.0}   27.99±0.83            39.92±1.25             38.71±5.76                         79.44\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 6.0}   27.54±0.84            40.38±1.55             43.93±6.38                         91.5\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 7.0}   28.05±0.92            41.93±1.85             42.06±7.45                        103.37\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 8.0}   28.46±1.04            43.06±1.90             43.56±6.35                        116.12\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 9.0}   28.46±1.34            45.96±6.41             46.21±8.71                        129.01\n",
      "{'alpha': '7.94e+01', 'base_kernel': 'sp', 'height': 10.0}  31.13±14.51           49.17±16.30            62.83±88.58                       142.76\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 0.0}   35.48±0.55            39.76±0.63             38.31±7.83                         13.25\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 1.0}   29.72±0.64            36.30±0.97             37.07±5.53                         26.25\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 2.0}   28.32±0.63            35.99±0.98             37.92±5.60                         39.79\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 3.0}   28.22±0.54            36.97±0.69             34.99±5.14                         52.12\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 4.0}   27.87±0.72            37.75±1.16             36.27±5.89                         64.38\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 5.0}   27.91±0.77            38.52±1.00             37.30±5.45                         79.44\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 6.0}   27.38±0.75            38.70±1.34             42.26±5.96                         91.5\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 7.0}   27.83±0.83            40.09±1.52             40.30±6.60                        103.37\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 8.0}   28.09±0.85            40.81±1.51             41.06±5.16                        116.12\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 9.0}   27.93±1.10            42.01±2.33             42.72±6.70                        129.01\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'sp', 'height': 10.0}  28.15±1.42            42.94±3.37             45.42±11.43                       142.76\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "calculate performance: 100%|██████████| 13530/13530 [16:20<00:00, 14.10it/s]"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = weisfeilerlehmankernel\n",
    "param_grid_precomputed = {'height': np.linspace(0, 10, 11), 'base_kernel': ['sp']}\n",
    "param_grid = {'alpha': np.logspace(-2, 2, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 0.3646550178527832 seconds ---\n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 985/1000 [00:01<00:00, 664.77it/s]\n",
      " Mean performance on train set: 17.681582\n",
      "With standard deviation: 0.713183\n",
      "\n",
      " Mean performance on test set: 15.685879\n",
      "With standard deviation: 4.139197\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 681.36it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 0.7535510063171387 seconds ---\n",
      "[[ 10.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  16.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  10. ...  22.  22.  24.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 130. 130. 122.]\n",
      " [ 20.  20.  22. ... 130. 130. 122.]\n",
      " [ 20.  20.  24. ... 122. 122. 154.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▍| 945/1000 [00:01<00:00, 713.00it/s]\n",
      " Mean performance on train set: 6.270014\n",
      "With standard deviation: 0.654734\n",
      "\n",
      " Mean performance on test set: 7.550458\n",
      "With standard deviation: 2.331786\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 719.46it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 1.3278343677520752 seconds ---\n",
      "[[ 15.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  24.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  15. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 159. 151. 124.]\n",
      " [ 20.  20.  22. ... 151. 153. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 185.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  95%|█████████▍| 949/1000 [00:01<00:00, 736.38it/s]\n",
      " Mean performance on train set: 4.450682\n",
      "With standard deviation: 0.882129\n",
      "\n",
      " Mean performance on test set: 9.728466\n",
      "With standard deviation: 2.057669\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 709.22it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 1.7653727531433105 seconds ---\n",
      "[[ 20.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  32.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  20. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 188. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 168. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 202.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  96%|█████████▌| 959/1000 [00:01<00:00, 724.60it/s]\n",
      " Mean performance on train set: 2.270586\n",
      "With standard deviation: 0.481516\n",
      "\n",
      " Mean performance on test set: 11.296110\n",
      "With standard deviation: 2.799944\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 670.29it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 2.2821996212005615 seconds ---\n",
      "[[ 25.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  40.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  25. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 217. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 183. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 213.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 983/1000 [00:01<00:00, 709.28it/s]\n",
      " Mean performance on train set: 1.074035\n",
      "With standard deviation: 0.637823\n",
      "\n",
      " Mean performance on test set: 12.808303\n",
      "With standard deviation: 3.446939\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 646.12it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 2.706934928894043 seconds ---\n",
      "[[ 30.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  48.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  30. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 246. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 198. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 224.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  95%|█████████▌| 953/1000 [00:01<00:00, 553.49it/s]\n",
      " Mean performance on train set: 0.700602\n",
      "With standard deviation: 0.572640\n",
      "\n",
      " Mean performance on test set: 14.017923\n",
      "With standard deviation: 3.675042\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 621.01it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 3.1140964031219482 seconds ---\n",
      "[[ 35.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  56.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  35. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 275. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 213. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 235.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance: 100%|█████████▉| 997/1000 [00:01<00:00, 595.50it/s]\n",
      " Mean performance on train set: 0.691515\n",
      "With standard deviation: 0.564620\n",
      "\n",
      " Mean performance on test set: 14.918434\n",
      "With standard deviation: 3.805352\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 586.05it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 7.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 3.5894455909729004 seconds ---\n",
      "[[ 40.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  64.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  40. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 304. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 228. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 246.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  99%|█████████▉| 991/1000 [00:01<00:00, 663.55it/s]\n",
      " Mean performance on train set: 0.691516\n",
      "With standard deviation: 0.564620\n",
      "\n",
      " Mean performance on test set: 15.629476\n",
      "With standard deviation: 3.865387\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 627.59it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 8.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.081295967102051 seconds ---\n",
      "[[ 45.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  72.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  45. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 333. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 243. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 257.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  96%|█████████▌| 961/1000 [00:01<00:00, 601.33it/s]\n",
      " Mean performance on train set: 0.691515\n",
      "With standard deviation: 0.564620\n",
      "\n",
      " Mean performance on test set: 16.214369\n",
      "With standard deviation: 3.928756\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 603.90it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 9.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.497286796569824 seconds ---\n",
      "[[ 50.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  80.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  50. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 362. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 258. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 268.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  93%|█████████▎| 931/1000 [00:01<00:00, 511.55it/s]\n",
      " Mean performance on train set: 0.691515\n",
      "With standard deviation: 0.564620\n",
      "\n",
      " Mean performance on test set: 16.725744\n",
      "With standard deviation: 3.993095\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 550.66it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 185 built in 4.984841585159302 seconds ---\n",
      "[[ 55.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  88.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  55. ...  22.  22.  26.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 391. 159. 124.]\n",
      " [ 20.  20.  22. ... 159. 273. 124.]\n",
      " [ 20.  20.  26. ... 124. 124. 279.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▍| 942/1000 [00:01<00:00, 708.78it/s]\n",
      " Mean performance on train set: 0.691516\n",
      "With standard deviation: 0.564621\n",
      "\n",
      " Mean performance on test set: 17.186401\n",
      "With standard deviation: 4.056724\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 711.43it/s]\n",
      "\n",
      "\n",
      "  height    rmse_test    std_test    rmse_train    std_train    k_time\n",
      "--------  -----------  ----------  ------------  -----------  --------\n",
      "       0     15.6859      4.1392      17.6816       0.713183  0.364655\n",
      "       1      7.55046     2.33179      6.27001      0.654734  0.753551\n",
      "       2      9.72847     2.05767      4.45068      0.882129  1.32783\n",
      "       3     11.2961      2.79994      2.27059      0.481516  1.76537\n",
      "       4     12.8083      3.44694      1.07403      0.637823  2.2822\n",
      "       5     14.0179      3.67504      0.700602     0.57264   2.70693\n",
      "       6     14.9184      3.80535      0.691515     0.56462   3.1141\n",
      "       7     15.6295      3.86539      0.691516     0.56462   3.58945\n",
      "       8     16.2144      3.92876      0.691515     0.56462   4.0813\n",
      "       9     16.7257      3.9931       0.691515     0.56462   4.49729\n",
      "      10     17.1864      4.05672      0.691516     0.564621  4.98484\n"
     ]
    }
   ],
   "source": [
    "# wl subtree kernel\n",
    "%load_ext line_profiler\n",
    "\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel, _wl_subtreekernel_do\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type')\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, weisfeilerlehmankernel, kernel_para, \\\n",
    "    hyper_name = 'height', hyper_range = np.linspace(0, 10, 11), normalize = False)\n",
    "\n",
    "# %lprun -f _wl_subtreekernel_do \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, weisfeilerlehmankernel, kernel_para, \\\n",
    "#     hyper_name = 'height', hyper_range = np.linspace(0, 10, 11), normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 185 built in 13.473959922790527 seconds ---\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  99%|█████████▊| 986/1000 [00:01<00:00, 802.19it/s]\n",
      " Mean performance on train set: 28.360361\n",
      "With standard deviation: 1.357183\n",
      "\n",
      " Mean performance on test set: 35.191954\n",
      "With standard deviation: 4.495767\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 805.00it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 185 built in 27.61470603942871 seconds ---\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  96%|█████████▌| 959/1000 [00:01<00:00, 734.53it/s]\n",
      " Mean performance on train set: 27.933534\n",
      "With standard deviation: 1.448359\n",
      "\n",
      " Mean performance on test set: 35.180815\n",
      "With standard deviation: 4.500453\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 724.44it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 185 built in 41.05776810646057 seconds ---\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▎| 935/1000 [00:01<00:00, 740.19it/s]\n",
      " Mean performance on train set: 28.111311\n",
      "With standard deviation: 1.508915\n",
      "\n",
      " Mean performance on test set: 35.163150\n",
      "With standard deviation: 4.502054\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 717.12it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 185 built in 54.166242837905884 seconds ---\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  93%|█████████▎| 932/1000 [00:01<00:00, 781.12it/s]\n",
      " Mean performance on train set: 28.390274\n",
      "With standard deviation: 1.365711\n",
      "\n",
      " Mean performance on test set: 35.194634\n",
      "With standard deviation: 4.498007\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 773.40it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 185 built in 70.05019497871399 seconds ---\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  97%|█████████▋| 970/1000 [00:01<00:00, 880.10it/s]\n",
      " Mean performance on train set: 27.974611\n",
      "With standard deviation: 1.462223\n",
      "\n",
      " Mean performance on test set: 35.175314\n",
      "With standard deviation: 4.501113\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 874.69it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman sp kernel matrix of size 185 built in 79.92547488212585 seconds ---\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance: 100%|█████████▉| 996/1000 [00:01<00:00, 884.85it/s]\n",
      " Mean performance on train set: 28.018415\n",
      "With standard deviation: 1.455644\n",
      "\n",
      " Mean performance on test set: 35.199713\n",
      "With standard deviation: 4.507104\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 897.03it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-e7b9d5ef03e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mkernel_para\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'atom'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'bond_type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase_kernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'sp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mkernel_train_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatafile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_file_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweisfeilerlehmankernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_para\u001b[0m\u001b[0;34m,\u001b[0m     \u001b[0mhyper_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'height'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhyper_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# %lprun -f _wl_subtreekernel_do \\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/utils.py\u001b[0m in \u001b[0;36mkernel_train_test\u001b[0;34m(datafile, kernel_file_path, kernel_func, kernel_para, trials, splits, alpha_grid, C_grid, hyper_name, hyper_range, normalize, datafile_y, model_type)\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhyper_name\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m                 \u001b[0mkernel_para\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhyper_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhyper_para\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m             \u001b[0mKmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkernel_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkernel_para\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m             \u001b[0mkernel_time_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/weisfeilerLehmanKernel.py\u001b[0m in \u001b[0;36mweisfeilerlehmankernel\u001b[0;34m(node_label, edge_label, height, base_kernel, *args)\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;31m# for WL shortest path kernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mbase_kernel\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0mKmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_wl_spkernel_do\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;31m# for WL edge kernel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/weisfeilerLehmanKernel.py\u001b[0m in \u001b[0;36m_wl_spkernel_do\u001b[0;34m(Gn, node_label, edge_label, height)\u001b[0m\n\u001b[1;32m    254\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0me1\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0me2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m                         \u001b[0;32mif\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m                             \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m                 \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# WL sp kernel\n",
    "%load_ext line_profiler\n",
    "\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel, _wl_subtreekernel_do\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', base_kernel = 'sp')\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, weisfeilerlehmankernel, kernel_para, \\\n",
    "    hyper_name = 'height', hyper_range = np.linspace(0, 10, 11), normalize = False)\n",
    "\n",
    "# %lprun -f _wl_subtreekernel_do \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, weisfeilerlehmankernel, kernel_para, \\\n",
    "#     hyper_name = 'height', hyper_range = np.linspace(0, 10, 11), normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 0.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 0.8530018329620361 seconds ---\n",
      "[[ 2.  1.  2. ...  0.  0.  1.]\n",
      " [ 1.  3.  1. ...  0.  0.  2.]\n",
      " [ 2.  1.  2. ...  0.  0.  1.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 10.  7.  0.]\n",
      " [ 0.  0.  0. ...  7. 10.  1.]\n",
      " [ 1.  2.  1. ...  0.  1. 10.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  95%|█████████▍| 947/1000 [00:01<00:00, 719.29it/s]\n",
      " Mean performance on train set: 29.997498\n",
      "With standard deviation: 0.902340\n",
      "\n",
      " Mean performance on test set: 33.407740\n",
      "With standard deviation: 4.732717\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 653.54it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 1.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 1.717505931854248 seconds ---\n",
      "[[ 4.  2.  4. ...  0.  0.  2.]\n",
      " [ 2.  6.  2. ...  0.  0.  4.]\n",
      " [ 4.  2.  4. ...  0.  0.  2.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 20. 14.  0.]\n",
      " [ 0.  0.  0. ... 14. 20.  2.]\n",
      " [ 2.  4.  2. ...  0.  2. 20.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  96%|█████████▌| 956/1000 [00:01<00:00, 721.27it/s]\n",
      " Mean performance on train set: 30.160338\n",
      "With standard deviation: 1.094235\n",
      "\n",
      " Mean performance on test set: 33.423458\n",
      "With standard deviation: 4.721311\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 723.53it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 2.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 2.6603214740753174 seconds ---\n",
      "[[ 6.  3.  6. ...  0.  0.  3.]\n",
      " [ 3.  9.  3. ...  0.  0.  6.]\n",
      " [ 6.  3.  6. ...  0.  0.  3.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 30. 21.  0.]\n",
      " [ 0.  0.  0. ... 21. 30.  3.]\n",
      " [ 3.  6.  3. ...  0.  3. 30.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▍| 944/1000 [00:01<00:00, 650.98it/s]\n",
      " Mean performance on train set: 29.928570\n",
      "With standard deviation: 0.787941\n",
      "\n",
      " Mean performance on test set: 33.433014\n",
      "With standard deviation: 4.724408\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 688.71it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 3.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 3.477631092071533 seconds ---\n",
      "[[ 8.  4.  8. ...  0.  0.  4.]\n",
      " [ 4. 12.  4. ...  0.  0.  8.]\n",
      " [ 8.  4.  8. ...  0.  0.  4.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 40. 28.  0.]\n",
      " [ 0.  0.  0. ... 28. 40.  4.]\n",
      " [ 4.  8.  4. ...  0.  4. 40.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  95%|█████████▌| 954/1000 [00:01<00:00, 725.15it/s]\n",
      " Mean performance on train set: 30.011409\n",
      "With standard deviation: 0.909674\n",
      "\n",
      " Mean performance on test set: 33.407319\n",
      "With standard deviation: 4.732434\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 720.71it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 4.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 4.5436692237854 seconds ---\n",
      "[[10.  5. 10. ...  0.  0.  5.]\n",
      " [ 5. 15.  5. ...  0.  0. 10.]\n",
      " [10.  5. 10. ...  0.  0.  5.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 50. 35.  0.]\n",
      " [ 0.  0.  0. ... 35. 50.  5.]\n",
      " [ 5. 10.  5. ...  0.  5. 50.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▎| 936/1000 [00:01<00:00, 568.04it/s]\n",
      " Mean performance on train set: 30.184162\n",
      "With standard deviation: 1.108902\n",
      "\n",
      " Mean performance on test set: 33.425625\n",
      "With standard deviation: 4.721660\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 564.24it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 5.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 5.6617820262908936 seconds ---\n",
      "[[12.  6. 12. ...  0.  0.  6.]\n",
      " [ 6. 18.  6. ...  0.  0. 12.]\n",
      " [12.  6. 12. ...  0.  0.  6.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 60. 42.  0.]\n",
      " [ 0.  0.  0. ... 42. 60.  6.]\n",
      " [ 6. 12.  6. ...  0.  6. 60.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  99%|█████████▉| 993/1000 [00:01<00:00, 519.25it/s]\n",
      " Mean performance on train set: 30.041068\n",
      "With standard deviation: 1.018451\n",
      "\n",
      " Mean performance on test set: 33.406717\n",
      "With standard deviation: 4.726409\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 548.91it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 6.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 6.148027420043945 seconds ---\n",
      "[[14.  7. 14. ...  0.  0.  7.]\n",
      " [ 7. 21.  7. ...  0.  0. 14.]\n",
      " [14.  7. 14. ...  0.  0.  7.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 70. 49.  0.]\n",
      " [ 0.  0.  0. ... 49. 70.  7.]\n",
      " [ 7. 14.  7. ...  0.  7. 70.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 985/1000 [00:01<00:00, 498.31it/s]\n",
      " Mean performance on train set: 29.905596\n",
      "With standard deviation: 0.782179\n",
      "\n",
      " Mean performance on test set: 33.418992\n",
      "With standard deviation: 4.730753\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 534.86it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 7.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 7.603543519973755 seconds ---\n",
      "[[16.  8. 16. ...  0.  0.  8.]\n",
      " [ 8. 24.  8. ...  0.  0. 16.]\n",
      " [16.  8. 16. ...  0.  0.  8.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 80. 56.  0.]\n",
      " [ 0.  0.  0. ... 56. 80.  8.]\n",
      " [ 8. 16.  8. ...  0.  8. 80.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  95%|█████████▌| 953/1000 [00:01<00:00, 586.15it/s]\n",
      " Mean performance on train set: 30.175921\n",
      "With standard deviation: 1.103820\n",
      "\n",
      " Mean performance on test set: 33.424820\n",
      "With standard deviation: 4.721550\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 546.00it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 8.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 7.972221612930298 seconds ---\n",
      "[[18.  9. 18. ...  0.  0.  9.]\n",
      " [ 9. 27.  9. ...  0.  0. 18.]\n",
      " [18.  9. 18. ...  0.  0.  9.]\n",
      " ...\n",
      " [ 0.  0.  0. ... 90. 63.  0.]\n",
      " [ 0.  0.  0. ... 63. 90.  9.]\n",
      " [ 9. 18.  9. ...  0.  9. 90.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 980/1000 [00:01<00:00, 490.30it/s]\n",
      " Mean performance on train set: 30.136537\n",
      "With standard deviation: 1.074854\n",
      "\n",
      " Mean performance on test set: 33.412196\n",
      "With standard deviation: 4.715539\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 536.66it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 9.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 9.070842504501343 seconds ---\n",
      "[[ 20.  10.  20. ...   0.   0.  10.]\n",
      " [ 10.  30.  10. ...   0.   0.  20.]\n",
      " [ 20.  10.  20. ...   0.   0.  10.]\n",
      " ...\n",
      " [  0.   0.   0. ... 100.  70.   0.]\n",
      " [  0.   0.   0. ...  70. 100.  10.]\n",
      " [ 10.  20.  10. ...   0.  10. 100.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  98%|█████████▊| 975/1000 [00:01<00:00, 527.13it/s]\n",
      " Mean performance on train set: 30.032887\n",
      "With standard deviation: 0.921065\n",
      "\n",
      " Mean performance on test set: 33.407050\n",
      "With standard deviation: 4.731928\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 600.62it/s]\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when height = 10.0 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- Weisfeiler-Lehman edge kernel matrix of size 185 built in 10.02536916732788 seconds ---\n",
      "[[ 22.  11.  22. ...   0.   0.  11.]\n",
      " [ 11.  33.  11. ...   0.   0.  22.]\n",
      " [ 22.  11.  22. ...   0.   0.  11.]\n",
      " ...\n",
      " [  0.   0.   0. ... 110.  77.   0.]\n",
      " [  0.   0.   0. ...  77. 110.  11.]\n",
      " [ 11.  22.  11. ...   0.  11. 110.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance:  97%|█████████▋| 970/1000 [00:01<00:00, 694.38it/s]\n",
      " Mean performance on train set: 29.924232\n",
      "With standard deviation: 0.790843\n",
      "\n",
      " Mean performance on test set: 33.416469\n",
      "With standard deviation: 4.731694\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 678.72it/s]\n",
      "\n",
      "\n",
      "  height    rmse_test    std_test    rmse_train    std_train     k_time\n",
      "--------  -----------  ----------  ------------  -----------  ---------\n",
      "       0      33.4077     4.73272       29.9975     0.90234    0.853002\n",
      "       1      33.4235     4.72131       30.1603     1.09423    1.71751\n",
      "       2      33.433      4.72441       29.9286     0.787941   2.66032\n",
      "       3      33.4073     4.73243       30.0114     0.909674   3.47763\n",
      "       4      33.4256     4.72166       30.1842     1.1089     4.54367\n",
      "       5      33.4067     4.72641       30.0411     1.01845    5.66178\n",
      "       6      33.419      4.73075       29.9056     0.782179   6.14803\n",
      "       7      33.4248     4.72155       30.1759     1.10382    7.60354\n",
      "       8      33.4122     4.71554       30.1365     1.07485    7.97222\n",
      "       9      33.4071     4.73193       30.0329     0.921065   9.07084\n",
      "      10      33.4165     4.73169       29.9242     0.790843  10.0254\n"
     ]
    }
   ],
   "source": [
    "# WL edge kernel\n",
    "%load_ext line_profiler\n",
    "\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel, _wl_subtreekernel_do\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', base_kernel = 'edge')\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, weisfeilerlehmankernel, kernel_para, \\\n",
    "    hyper_name = 'height', hyper_range = np.linspace(0, 10, 11), normalize = False)\n",
    "\n",
    "# %lprun -f _wl_subtreekernel_do \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, weisfeilerlehmankernel, kernel_para, \\\n",
    "#     hyper_name = 'height', hyper_range = np.linspace(0, 10, 11), normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unindent does not match any outer indentation level (<tokenize>, line 16)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<tokenize>\"\u001b[0;36m, line \u001b[0;32m16\u001b[0m\n\u001b[0;31m    10     34.0963      6.93115      11.4257     11.2624    4.94888\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unindent does not match any outer indentation level\n"
     ]
    }
   ],
   "source": [
    "# results\n",
    "\n",
    "# subtree with y normalization\n",
    "  height    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "--------  -----------  ----------  ------------  -----------  --------\n",
    "       0     36.2108      7.33179      38.6059      1.57064   0.379475\n",
    "       1      9.00098     6.37145       6.76379     1.96568   0.844898\n",
    "       2     19.8113      4.04911       5.28757     1.81899   1.35308\n",
    "       3     25.0455      4.94276       2.3274      0.805733  1.81136\n",
    "       4     28.2255      6.5212        0.85156     0.423465  2.23098\n",
    "       5     30.6354      6.73647       3.35947     8.17561   2.71575\n",
    "       6     32.1027      6.85601       3.54105     8.71922   3.11459\n",
    "       7     32.9709      6.89606       6.94372     9.94045   3.55571\n",
    "       8     33.5112      6.90753       6.97339     9.76975   3.79657\n",
    "       9     33.8502      6.91427      11.8345     11.6213    4.41555\n",
    "      10     34.0963      6.93115      11.4257     11.2624    4.94888\n",
    "\n",
    "# subtree without y normalization\n",
    "  height    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "--------  -----------  ----------  ------------  -----------  --------\n",
    "       0     15.6859      4.1392      17.6816       0.713183  0.360443\n",
    "       1      7.55046     2.33179      6.27001      0.654734  0.837389\n",
    "       2      9.72847     2.05767      4.45068      0.882129  1.25317\n",
    "       3     11.2961      2.79994      2.27059      0.481516  1.79971\n",
    "       4     12.8083      3.44694      1.07403      0.637823  2.35346\n",
    "       5     14.0179      3.67504      0.700602     0.57264   2.78285\n",
    "       6     14.9184      3.80535      0.691515     0.56462   3.20764\n",
    "       7     15.6295      3.86539      0.691516     0.56462   3.71648\n",
    "       8     16.2144      3.92876      0.691515     0.56462   3.99213\n",
    "       9     16.7257      3.9931       0.691515     0.56462   4.26315\n",
    "      10     17.1864      4.05672      0.691516     0.564621  5.00918\n",
    "    \n",
    "# sp\n",
    "  height    rmse_test    std_test    rmse_train    std_train    k_time\n",
    "--------  -----------  ----------  ------------  -----------  --------\n",
    "       0      35.192      4.49577       28.3604      1.35718   13.5041\n",
    "       1      35.1808     4.50045       27.9335      1.44836   26.8292\n",
    "       2      35.1632     4.50205       28.1113      1.50891   40.2356\n",
    "       3      35.1946     4.49801       28.3903      1.36571   54.6704\n",
    "       4      35.1753     4.50111       27.9746      1.46222   67.1522\n",
    "       5      35.1997     4.5071        28.0184      1.45564   80.0881\n",
    "       6      35.1645     4.49849       28.3731      1.60057   92.1925\n",
    "       7      35.1771     4.5009        27.9604      1.45742  105.812\n",
    "       8      35.1968     4.50526       28.1991      1.5149   119.022\n",
    "       9      35.1956     4.50197       28.2665      1.30769  131.228\n",
    "      10      35.1676     4.49723       28.4163      1.61596  144.964\n",
    "    \n",
    "# path\n",
    "  height    rmse_test    std_test    rmse_train    std_train     k_time\n",
    "--------  -----------  ----------  ------------  -----------  ---------\n",
    "       0      33.4077     4.73272       29.9975     0.90234    0.853002\n",
    "       1      33.4235     4.72131       30.1603     1.09423    1.71751\n",
    "       2      33.433      4.72441       29.9286     0.787941   2.66032\n",
    "       3      33.4073     4.73243       30.0114     0.909674   3.47763\n",
    "       4      33.4256     4.72166       30.1842     1.1089     4.54367\n",
    "       5      33.4067     4.72641       30.0411     1.01845    5.66178\n",
    "       6      33.419      4.73075       29.9056     0.782179   6.14803\n",
    "       7      33.4248     4.72155       30.1759     1.10382    7.60354\n",
    "       8      33.4122     4.71554       30.1365     1.07485    7.97222\n",
    "       9      33.4071     4.73193       30.0329     0.921065   9.07084\n",
    "      10      33.4165     4.73169       29.9242     0.790843  10.0254"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'O', 'C'}\n",
      "{'O', 'C'}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-e54963002171>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabelset1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabelset2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mkernel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mspkernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/spKernel.py\u001b[0m in \u001b[0;36mspkernel\u001b[0;34m(edge_weight, *args)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mGn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mgetSPGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0medge_weight\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mG\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;31m# get shortest path graphs of Gn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/spKernel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mGn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m \u001b[0mgetSPGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0medge_weight\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mG\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;31m# get shortest path graphs of Gn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/utils.py\u001b[0m in \u001b[0;36mgetSPGraph\u001b[0;34m(G, edge_weight)\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0mBorgwardt\u001b[0m \u001b[0mKM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKriegel\u001b[0m \u001b[0mHP\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mShortest\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpath\u001b[0m \u001b[0mkernels\u001b[0m \u001b[0mon\u001b[0m \u001b[0mgraphs\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mInData\u001b[0m \u001b[0mMining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFifth\u001b[0m \u001b[0mIEEE\u001b[0m \u001b[0mInternational\u001b[0m \u001b[0mConference\u001b[0m \u001b[0mon\u001b[0m \u001b[0;36m2005\u001b[0m \u001b[0mNov\u001b[0m \u001b[0;36m27\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIEEE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \"\"\"\n\u001b[0;32m---> 37\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfloydTransformation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0medge_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfloydTransformation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'bond_type'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/utils.py\u001b[0m in \u001b[0;36mfloydTransformation\u001b[0;34m(G, edge_weight)\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0mBorgwardt\u001b[0m \u001b[0mKM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKriegel\u001b[0m \u001b[0mHP\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mShortest\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpath\u001b[0m \u001b[0mkernels\u001b[0m \u001b[0mon\u001b[0m \u001b[0mgraphs\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mInData\u001b[0m \u001b[0mMining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFifth\u001b[0m \u001b[0mIEEE\u001b[0m \u001b[0mInternational\u001b[0m \u001b[0mConference\u001b[0m \u001b[0mon\u001b[0m \u001b[0;36m2005\u001b[0m \u001b[0mNov\u001b[0m \u001b[0;36m27\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mpp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIEEE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \"\"\"\n\u001b[0;32m---> 58\u001b[0;31m     \u001b[0mspMatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloyd_warshall_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0medge_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m     \u001b[0mS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0mS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_nodes_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/networkx/algorithms/shortest_paths/dense.py\u001b[0m in \u001b[0;36mfloyd_warshall_numpy\u001b[0;34m(G, nodelist, weight)\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;31m# nonedges are not given the value 0 as well.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     A = nx.to_numpy_matrix(G, nodelist=nodelist, multigraph_weight=min,\n\u001b[0;32m---> 54\u001b[0;31m                            weight=weight, nonedge=np.inf)\n\u001b[0m\u001b[1;32m     55\u001b[0m     \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mI\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/networkx/convert_matrix.py\u001b[0m in \u001b[0;36mto_numpy_matrix\u001b[0;34m(G, nodelist, dtype, order, multigraph_weight, weight, nonedge)\u001b[0m\n\u001b[1;32m    446\u001b[0m     A = to_numpy_array(G, nodelist=nodelist, dtype=dtype, order=order,\n\u001b[1;32m    447\u001b[0m                        \u001b[0mmultigraph_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultigraph_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m                        nonedge=nonedge)\n\u001b[0m\u001b[1;32m    449\u001b[0m     \u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/networkx/convert_matrix.py\u001b[0m in \u001b[0;36mto_numpy_array\u001b[0;34m(G, nodelist, dtype, order, multigraph_weight, weight, nonedge)\u001b[0m\n\u001b[1;32m   1061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnodelist\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m         \u001b[0mnodelist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1064\u001b[0m     \u001b[0mnodeset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnodelist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1065\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnodelist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnodeset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import networkx as nx\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.graphfiles import loadDataset\n",
    "from pygraph.kernels.spkernel import spkernel\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def weisfeilerlehman_test(G):\n",
    "    '''\n",
    "    Weisfeiler-Lehman test of graph isomorphism.\n",
    "    '''\n",
    "\n",
    "    nx.draw_networkx(G)\n",
    "    plt.show()\n",
    "    nx.draw_networkx_labels(G, nx.spring_layout(G), labels = nx.get_node_attributes(G,'label'))\n",
    "    print(G.nodes(data = True))\n",
    "    \n",
    "    set_multisets = []\n",
    "    for node in G.nodes(data = True):\n",
    "        # Multiset-label determination.\n",
    "        multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
    "        # sorting each multiset\n",
    "        multiset.sort()\n",
    "        multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
    "        set_multisets.append(multiset)\n",
    "        \n",
    "    # label compression\n",
    "#     set_multisets.sort() # this is unnecessary\n",
    "    set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
    "    set_compressed = { value : str(set_unique.index(value)) for value in set_unique } # assign indices as the new labels\n",
    "#     print(set_compressed)\n",
    "#     print(set_multisets)\n",
    "    \n",
    "    # relabel nodes with multisets\n",
    "    for node in G.nodes(data = True):\n",
    "        node[1]['label'] = set_multisets[node[0]]\n",
    "    print(' -> ')\n",
    "    nx.draw_networkx(G)\n",
    "    plt.show()\n",
    "    print(G.nodes(data = True))\n",
    "\n",
    "    \n",
    "    # relabel nodes\n",
    "    for node in G.nodes(data = True):\n",
    "        node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
    "        \n",
    "    print(' -> ')\n",
    "    nx.draw_networkx(G)\n",
    "    plt.show()\n",
    "    print(G.nodes(data = True))\n",
    "\n",
    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
    "G1 = dataset[12]\n",
    "G2 = dataset[55]\n",
    "\n",
    "# init.\n",
    "kernel = 0 # init kernel\n",
    "num_nodes1 = G1.number_of_nodes()\n",
    "num_nodes2 = G2.number_of_nodes()\n",
    "\n",
    "# the first iteration.\n",
    "labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
    "labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
    "print(labelset1)\n",
    "print(labelset2)\n",
    "kernel += spkernel(G1, G2)\n",
    "print(kernel)\n",
    "\n",
    "\n",
    "\n",
    "for height in range(0, min(num_nodes1, num_nodes2)): #Q how to determine the upper bound of the height?\n",
    "    if labelset1 != labelset2:\n",
    "        break\n",
    "        \n",
    "    # Weisfeiler-Lehman test of graph isomorphism.\n",
    "    weisfeilerlehman_test(G1)\n",
    "    weisfeilerlehman_test(G2)\n",
    "    \n",
    "    # calculate kernel\n",
    "    kernel += spkernel(G1, G2)\n",
    "    \n",
    "    # get label sets of both graphs\n",
    "    labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
    "    labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
    "#     print(labelset1)\n",
    "#     print(labelset2)\n",
    "\n",
    "print(kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'O', 6: 'O'}\n",
      "{0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C', 5: 'C', 6: 'S', 7: 'S'}\n",
      "\n",
      " --- height = 0 --- \n",
      "\n",
      " --- for graph 0 --- \n",
      "\n",
      "labels_ori: ['C', 'C', 'C', 'C', 'C', 'O', 'O']\n",
      "all_labels_ori: {'C', 'O'}\n",
      "num_of_each_label: {'C': 5, 'O': 2}\n",
      "all_num_of_each_label: [{'C': 5, 'O': 2}]\n",
      "num_of_labels: 2\n",
      "all_labels_ori: {'C', 'O'}\n",
      "\n",
      " --- for graph 1 --- \n",
      "\n",
      "labels_ori: ['C', 'C', 'C', 'C', 'C', 'C', 'S', 'S']\n",
      "all_labels_ori: {'C', 'O', 'S'}\n",
      "num_of_each_label: {'C': 6, 'S': 2}\n",
      "all_num_of_each_label: [{'C': 5, 'O': 2}, {'C': 6, 'S': 2}]\n",
      "num_of_labels: 2\n",
      "all_labels_ori: {'C', 'O', 'S'}\n",
      "\n",
      " all_num_of_labels_occured: 3\n",
      "\n",
      " --- calculating kernel matrix ---\n",
      "\n",
      " labels: {'C', 'O'}\n",
      "vector1: [[5 2]]\n",
      "vector2: [[5 2]]\n",
      "Kmatrix: [[ 29.   0.]\n",
      " [  0.   0.]]\n",
      "\n",
      " labels: {'C', 'O', 'S'}\n",
      "vector1: [[5 2 0]]\n",
      "vector2: [[6 0 2]]\n",
      "Kmatrix: [[ 29.  30.]\n",
      " [ 30.   0.]]\n",
      "\n",
      " labels: {'C', 'S'}\n",
      "vector1: [[6 2]]\n",
      "vector2: [[6 2]]\n",
      "Kmatrix: [[ 29.  30.]\n",
      " [ 30.  40.]]\n",
      "\n",
      " --- height = 1 --- \n",
      "\n",
      " --- for graph 0 --- \n",
      "\n",
      "multiset: ['CC', 'CC', 'CCO', 'CCO', 'COO', 'OCC', 'OCC']\n",
      "set_unique: ['OCC', 'COO', 'CCO', 'CC']\n",
      "set_compressed: {'OCC': '4', 'COO': '5', 'CCO': '6', 'CC': '7'}\n",
      "all_set_compressed: {'OCC': '4', 'COO': '5', 'CCO': '6', 'CC': '7'}\n",
      "num_of_labels_occured: 7\n",
      "\n",
      " compressed labels: {0: '7', 1: '7', 2: '6', 3: '6', 4: '5', 5: '4', 6: '4'}\n",
      "labels_comp: ['7', '7', '6', '6', '5', '4', '4']\n",
      "all_labels_ori: {'5', '4', '6', '7'}\n",
      "num_of_each_label: {'5': 1, '4': 2, '6': 2, '7': 2}\n",
      "all_num_of_each_label: [{'5': 1, '4': 2, '6': 2, '7': 2}]\n",
      "\n",
      " --- for graph 1 --- \n",
      "\n",
      "multiset: ['CC', 'CC', 'CC', 'CCS', 'CCS', 'CCSS', 'SCC', 'SCC']\n",
      "set_unique: ['SCC', 'CC', 'CCS', 'CCSS']\n",
      "set_compressed: {'SCC': '8', 'CC': '7', 'CCS': '9', 'CCSS': '10'}\n",
      "all_set_compressed: {'SCC': '8', 'COO': '5', 'CCS': '9', 'OCC': '4', 'CCO': '6', 'CCSS': '10', 'CC': '7'}\n",
      "num_of_labels_occured: 10\n",
      "\n",
      " compressed labels: {0: '7', 1: '7', 2: '7', 3: '9', 4: '9', 5: '10', 6: '8', 7: '8'}\n",
      "labels_comp: ['7', '7', '7', '9', '9', '10', '8', '8']\n",
      "all_labels_ori: {'10', '4', '7', '9', '6', '5', '8'}\n",
      "num_of_each_label: {'10': 1, '9': 2, '7': 3, '8': 2}\n",
      "all_num_of_each_label: [{'5': 1, '4': 2, '6': 2, '7': 2}, {'10': 1, '9': 2, '7': 3, '8': 2}]\n",
      "\n",
      " all_num_of_labels_occured: 10\n",
      "\n",
      " --- calculating kernel matrix ---\n",
      "\n",
      " labels: {'5', '4', '6', '7'}\n",
      "vector1: [[1 2 2 2]]\n",
      "vector2: [[1 2 2 2]]\n",
      "\n",
      " labels: {'10', '4', '7', '9', '6', '5', '8'}\n",
      "vector1: [[0 2 2 0 2 1 0]]\n",
      "vector2: [[1 0 3 2 0 0 2]]\n",
      "\n",
      " labels: {'8', '10', '7', '9'}\n",
      "vector1: [[2 1 3 2]]\n",
      "vector2: [[2 1 3 2]]\n",
      "\n",
      " Kmatrix: [[ 42.  36.]\n",
      " [ 36.  58.]]\n",
      "\n",
      " --- height = 2 --- \n",
      "\n",
      " --- for graph 0 --- \n",
      "\n",
      "multiset: ['76', '76', '647', '647', '544', '456', '456']\n",
      "set_unique: ['647', '76', '456', '544']\n",
      "set_compressed: {'647': '11', '76': '12', '544': '14', '456': '13'}\n",
      "all_set_compressed: {'647': '11', '76': '12', '456': '13', '544': '14'}\n",
      "num_of_labels_occured: 14\n",
      "\n",
      " compressed labels: {0: '12', 1: '12', 2: '11', 3: '11', 4: '14', 5: '13', 6: '13'}\n",
      "labels_comp: ['12', '12', '11', '11', '14', '13', '13']\n",
      "all_labels_ori: {'14', '12', '11', '13'}\n",
      "num_of_each_label: {'14': 1, '13': 2, '12': 2, '11': 2}\n",
      "all_num_of_each_label: [{'14': 1, '13': 2, '12': 2, '11': 2}]\n",
      "\n",
      " --- for graph 1 --- \n",
      "\n",
      "multiset: ['79', '79', '710', '978', '978', '10788', '8109', '8109']\n",
      "set_unique: ['710', '8109', '79', '10788', '978']\n",
      "set_compressed: {'710': '15', '79': '17', '8109': '16', '978': '19', '10788': '18'}\n",
      "all_set_compressed: {'710': '15', '79': '17', '978': '19', '10788': '18', '8109': '16', '456': '13', '544': '14', '647': '11', '76': '12'}\n",
      "num_of_labels_occured: 19\n",
      "\n",
      " compressed labels: {0: '17', 1: '17', 2: '15', 3: '19', 4: '19', 5: '18', 6: '16', 7: '16'}\n",
      "labels_comp: ['17', '17', '15', '19', '19', '18', '16', '16']\n",
      "all_labels_ori: {'18', '19', '12', '13', '17', '11', '14', '16', '15'}\n",
      "num_of_each_label: {'15': 1, '17': 2, '19': 2, '16': 2, '18': 1}\n",
      "all_num_of_each_label: [{'14': 1, '13': 2, '12': 2, '11': 2}, {'15': 1, '17': 2, '19': 2, '16': 2, '18': 1}]\n",
      "\n",
      " all_num_of_labels_occured: 19\n",
      "\n",
      " --- calculating kernel matrix ---\n",
      "\n",
      " labels: {'14', '12', '11', '13'}\n",
      "vector1: [[1 2 2 2]]\n",
      "vector2: [[1 2 2 2]]\n",
      "\n",
      " labels: {'18', '19', '12', '13', '17', '11', '14', '16', '15'}\n",
      "vector1: [[0 0 2 2 0 2 1 0 0]]\n",
      "vector2: [[1 2 0 0 2 0 0 2 1]]\n",
      "\n",
      " labels: {'18', '17', '15', '16', '19'}\n",
      "vector1: [[1 2 1 2 2]]\n",
      "vector2: [[1 2 1 2 2]]\n",
      "\n",
      " Kmatrix: [[ 55.  36.]\n",
      " [ 36.  72.]]\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel built in 0.0034377574920654297 seconds ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 55.,  36.],\n",
       "       [ 36.,  72.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test of WL subtree kernel on many graphs\n",
    "\n",
    "import sys\n",
    "import pathlib\n",
    "from collections import Counter\n",
    "sys.path.insert(0, \"../\")\n",
    "\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from pygraph.kernels.spkernel import spkernel\n",
    "from pygraph.kernels.pathKernel import pathkernel\n",
    "\n",
    "def weisfeilerlehmankernel(*args, height = 0, base_kernel = 'subtree'):\n",
    "    \"\"\"Calculate Weisfeiler-Lehman kernels between graphs.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    Gn : List of NetworkX graph\n",
    "        List of graphs between which the kernels are calculated.\n",
    "    /\n",
    "    G1, G2 : NetworkX graphs\n",
    "        2 graphs between which the kernel is calculated.\n",
    "        \n",
    "    height : subtree height\n",
    "    \n",
    "    base_kernel : base kernel used in each iteration of WL kernel\n",
    "        the default base kernel is subtree kernel\n",
    "        \n",
    "    Return\n",
    "    ------\n",
    "    Kmatrix/Kernel : Numpy matrix/int\n",
    "        Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. / Weisfeiler-Lehman Kernel between 2 graphs.\n",
    "        \n",
    "    Notes\n",
    "    -----\n",
    "    This function now supports WL subtree kernel and WL shortest path kernel.\n",
    "    \n",
    "    References\n",
    "    ----------\n",
    "    [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. 2011;12(Sep):2539-61.\n",
    "    \"\"\"\n",
    "    if len(args) == 1: # for a list of graphs\n",
    "\n",
    "#         print(args)\n",
    "        start_time = time.time()\n",
    "        \n",
    "        # for WL subtree kernel\n",
    "        if base_kernel == 'subtree':           \n",
    "            Kmatrix = _wl_subtreekernel_do(args[0], height = height, base_kernel = 'subtree')\n",
    "            \n",
    "        # for WL edge kernel\n",
    "        elif base_kernel == 'edge':\n",
    "            print('edge')\n",
    "            \n",
    "        # for WL shortest path kernel\n",
    "        elif base_kernel == 'sp':\n",
    "            Gn = args[0]\n",
    "            Kmatrix = np.zeros((len(Gn), len(Gn)))\n",
    "            \n",
    "            for i in range(0, len(Gn)):\n",
    "                for j in range(i, len(Gn)):\n",
    "                    Kmatrix[i][j] = _weisfeilerlehmankernel_do(Gn[i], Gn[j])\n",
    "                    Kmatrix[j][i] = Kmatrix[i][j]\n",
    "\n",
    "        print(\"\\n --- Weisfeiler-Lehman %s kernel matrix of size %d built in %s seconds ---\" % (base_kernel, len(args[0]), (time.time() - start_time)))\n",
    "        \n",
    "        return Kmatrix\n",
    "        \n",
    "    else: # for only 2 graphs\n",
    "        \n",
    "        start_time = time.time()\n",
    "        \n",
    "        # for WL subtree kernel\n",
    "        if base_kernel == 'subtree':\n",
    "            \n",
    "            args = [args[0], args[1]]\n",
    "#             print(args)\n",
    "            kernel = _wl_subtreekernel_do(args, height = height, base_kernel = 'subtree')\n",
    "            \n",
    "        # for WL edge kernel\n",
    "        elif base_kernel == 'edge':\n",
    "            print('edge')\n",
    "            \n",
    "        # for WL shortest path kernel\n",
    "        elif base_kernel == 'sp':\n",
    "            \n",
    "\n",
    "            kernel = _pathkernel_do(args[0], args[1])\n",
    "\n",
    "        print(\"\\n --- Weisfeiler-Lehman %s kernel built in %s seconds ---\" % (base_kernel, time.time() - start_time))\n",
    "        \n",
    "        return kernel\n",
    "    \n",
    "    \n",
    "def _weisfeilerlehmankernel_do(G1, G2):\n",
    "    \"\"\"Calculate Weisfeiler-Lehman kernels between 2 graphs. This kernel use shortest path kernel to calculate kernel between two graphs in each iteration.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    G1, G2 : NetworkX graphs\n",
    "        2 graphs between which the kernel is calculated.\n",
    "        \n",
    "    Return\n",
    "    ------\n",
    "    Kernel : int\n",
    "        Weisfeiler-Lehman Kernel between 2 graphs.\n",
    "    \"\"\"\n",
    "    \n",
    "    # init.\n",
    "    kernel = 0 # init kernel\n",
    "    num_nodes1 = G1.number_of_nodes()\n",
    "    num_nodes2 = G2.number_of_nodes()\n",
    "    height = 12 #min(num_nodes1, num_nodes2)) #Q how to determine the upper bound of the height?\n",
    "    \n",
    "    # the first iteration.\n",
    "    labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
    "    labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
    "    kernel += pathkernel(G1, G2) # change your base kernel here (and one more below)\n",
    "    \n",
    "    for h in range(0, height):\n",
    "#         if labelset1 != labelset2:\n",
    "#             break\n",
    "\n",
    "        # Weisfeiler-Lehman test of graph isomorphism.\n",
    "        relabel(G1)\n",
    "        relabel(G2)\n",
    "\n",
    "        # calculate kernel\n",
    "        kernel += pathkernel(G1, G2) # change your base kernel here (and one more before)\n",
    "\n",
    "        # get label sets of both graphs\n",
    "        labelset1 = { G1.nodes(data = True)[i]['label'] for i in range(num_nodes1) }\n",
    "        labelset2 = { G2.nodes(data = True)[i]['label'] for i in range(num_nodes2) }\n",
    "    \n",
    "    return kernel\n",
    "\n",
    "\n",
    "def relabel(G):\n",
    "    '''\n",
    "    Relabel nodes in graph G in one iteration of the 1-dim. WL test of graph isomorphism.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    G : NetworkX graph\n",
    "        The graphs whose nodes are relabeled.\n",
    "    '''\n",
    "    \n",
    "    # get the set of original labels\n",
    "    labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
    "    print(labels_ori)\n",
    "    num_of_each_label = dict(Counter(labels_ori))\n",
    "    print(num_of_each_label)\n",
    "    num_of_labels = len(num_of_each_label)\n",
    "    print(num_of_labels)\n",
    "    \n",
    "    set_multisets = []\n",
    "    for node in G.nodes(data = True):\n",
    "        # Multiset-label determination.\n",
    "        multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
    "        # sorting each multiset\n",
    "        multiset.sort()\n",
    "        multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
    "        set_multisets.append(multiset)\n",
    "    print(set_multisets)\n",
    "        \n",
    "    # label compression\n",
    "#     set_multisets.sort() # this is unnecessary\n",
    "    set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
    "    print(set_unique)\n",
    "    set_compressed = { value : str(set_unique.index(value) + num_of_labels + 1) for value in set_unique } # assign new labels\n",
    "    print(set_compressed)\n",
    "    \n",
    "    # relabel nodes\n",
    "#     nx.relabel_nodes(G, set_compressed, copy = False)\n",
    "    for node in G.nodes(data = True):\n",
    "        node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
    "    print(nx.get_node_attributes(G, 'label'))\n",
    "\n",
    "    # get the set of compressed labels\n",
    "    labels_comp = list(nx.get_node_attributes(G, 'label').values())\n",
    "    print(labels_comp)\n",
    "    num_of_each_label.update(dict(Counter(labels_comp)))\n",
    "    print(num_of_each_label)\n",
    "    \n",
    "    \n",
    "def _wl_subtreekernel_do(*args, height = 0, base_kernel = 'subtree'):\n",
    "    \"\"\"Calculate Weisfeiler-Lehman subtree kernels between graphs.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    Gn : List of NetworkX graph\n",
    "        List of graphs between which the kernels are calculated.\n",
    "        \n",
    "    Return\n",
    "    ------\n",
    "    Kmatrix/Kernel : Numpy matrix/int\n",
    "        Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs.\n",
    "    \"\"\"\n",
    "    \n",
    "#     print(args)\n",
    "    Gn = args[0]\n",
    "#     print(Gn)\n",
    "\n",
    "    Kmatrix = np.zeros((len(Gn), len(Gn)))\n",
    "    all_num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs\n",
    "    \n",
    "    # initial for height = 0\n",
    "    print('\\n --- height = 0 --- ')\n",
    "    all_labels_ori = set() # all unique orignal labels in all graphs in this iteration\n",
    "    all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration\n",
    "    all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration\n",
    "    num_of_labels_occured = all_num_of_labels_occured # number of the set of letters that occur before as node labels at least once in all graphs\n",
    "\n",
    "    # for each graph\n",
    "    for idx, G in enumerate(Gn):\n",
    "        # get the set of original labels\n",
    "        print('\\n --- for graph %d --- \\n' % (idx))\n",
    "        labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
    "        print('labels_ori: %s' % (labels_ori))\n",
    "        all_labels_ori.update(labels_ori)\n",
    "        print('all_labels_ori: %s' % (all_labels_ori))\n",
    "        num_of_each_label = dict(Counter(labels_ori)) # number of occurence of each label in graph\n",
    "        print('num_of_each_label: %s' % (num_of_each_label))\n",
    "        all_num_of_each_label.append(num_of_each_label)\n",
    "        print('all_num_of_each_label: %s' % (all_num_of_each_label))\n",
    "        num_of_labels = len(num_of_each_label) # number of all unique labels\n",
    "        print('num_of_labels: %s' % (num_of_labels))\n",
    "        \n",
    "\n",
    "        all_labels_ori.update(labels_ori)\n",
    "        print('all_labels_ori: %s' % (all_labels_ori))\n",
    "        \n",
    "    all_num_of_labels_occured += len(all_labels_ori)\n",
    "    print('\\n all_num_of_labels_occured: %s' % (all_num_of_labels_occured))\n",
    "        \n",
    "    # calculate subtree kernel with the 0th iteration and add it to the final kernel\n",
    "    print('\\n --- calculating kernel matrix ---')\n",
    "    for i in range(0, len(Gn)):\n",
    "        for j in range(i, len(Gn)):\n",
    "            labels = set(list(all_num_of_each_label[i].keys()) + list(all_num_of_each_label[j].keys()))\n",
    "            print('\\n labels: %s' % (labels))\n",
    "            vector1 = np.matrix([ (all_num_of_each_label[i][label] if (label in all_num_of_each_label[i].keys()) else 0) for label in labels ])\n",
    "            vector2 = np.matrix([ (all_num_of_each_label[j][label] if (label in all_num_of_each_label[j].keys()) else 0) for label in labels ])\n",
    "            print('vector1: %s' % (vector1))\n",
    "            print('vector2: %s' % (vector2))\n",
    "            Kmatrix[i][j] += np.dot(vector1, vector2.transpose())\n",
    "            Kmatrix[j][i] = Kmatrix[i][j]\n",
    "            print('Kmatrix: %s' % (Kmatrix))\n",
    "\n",
    "    \n",
    "    # iterate each height\n",
    "    for h in range(1, height + 1):\n",
    "        print('\\n --- height = %d --- ' % (h))\n",
    "        all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration\n",
    "        num_of_labels_occured = all_num_of_labels_occured # number of the set of letters that occur before as node labels at least once in all graphs\n",
    "        all_labels_ori = set()\n",
    "        all_num_of_each_label = []\n",
    "        \n",
    "        # for each graph\n",
    "        for idx, G in enumerate(Gn):\n",
    "#             # get the set of original labels\n",
    "            print('\\n --- for graph %d --- \\n' % (idx))\n",
    "#             labels_ori = list(nx.get_node_attributes(G, 'label').values())\n",
    "#             print('labels_ori: %s' % (labels_ori))\n",
    "#             num_of_each_label = dict(Counter(labels_ori)) # number of occurence of each label in graph\n",
    "#             print('num_of_each_label: %s' % (num_of_each_label))\n",
    "#             num_of_labels = len(num_of_each_label) # number of all unique labels\n",
    "#             print('num_of_labels: %s' % (num_of_labels))\n",
    "            \n",
    "#             all_labels_ori.update(labels_ori)\n",
    "#             print('all_labels_ori: %s' % (all_labels_ori))\n",
    "#             #             num_of_labels_occured += num_of_labels #@todo not precise\n",
    "#             num_of_labels_occured = all_num_of_labels_occured + len(all_labels_ori) + len(all_set_compressed)\n",
    "#             print('num_of_labels_occured: %s' % (num_of_labels_occured))\n",
    "            \n",
    "            set_multisets = []\n",
    "            for node in G.nodes(data = True):\n",
    "                # Multiset-label determination.\n",
    "                multiset = [ G.node[neighbors]['label'] for neighbors in G[node[0]] ]\n",
    "                # sorting each multiset\n",
    "                multiset.sort()\n",
    "                multiset = node[1]['label'] + ''.join(multiset) # concatenate to a string and add the prefix \n",
    "                set_multisets.append(multiset)\n",
    "            print('multiset: %s' % (set_multisets))\n",
    "\n",
    "            # label compression\n",
    "        #     set_multisets.sort() # this is unnecessary\n",
    "            set_unique = list(set(set_multisets)) # set of unique multiset labels\n",
    "            print('set_unique: %s' % (set_unique))\n",
    "            # a dictionary mapping original labels to new ones. \n",
    "            set_compressed = {}\n",
    "            # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label \n",
    "            for value in set_unique:\n",
    "                if value in all_set_compressed.keys():\n",
    "                    set_compressed.update({ value : all_set_compressed[value] })\n",
    "                else:\n",
    "                    set_compressed.update({ value : str(num_of_labels_occured + 1) })\n",
    "                    num_of_labels_occured += 1\n",
    "#             set_compressed = { value : (all_set_compressed[value] if value in all_set_compressed.keys() else str(set_unique.index(value) + num_of_labels_occured + 1)) for value in set_unique }\n",
    "            print('set_compressed: %s' % (set_compressed))\n",
    "            \n",
    "            all_set_compressed.update(set_compressed)\n",
    "            print('all_set_compressed: %s' % (all_set_compressed))\n",
    "#             num_of_labels_occured += len(set_compressed) #@todo not precise\n",
    "            print('num_of_labels_occured: %s' % (num_of_labels_occured))\n",
    "            \n",
    "            # relabel nodes\n",
    "        #     nx.relabel_nodes(G, set_compressed, copy = False)\n",
    "            for node in G.nodes(data = True):\n",
    "                node[1]['label'] = set_compressed[set_multisets[node[0]]]\n",
    "            print('\\n compressed labels: %s' % (nx.get_node_attributes(G, 'label')))\n",
    "\n",
    "            # get the set of compressed labels\n",
    "            labels_comp = list(nx.get_node_attributes(G, 'label').values())\n",
    "            print('labels_comp: %s' % (labels_comp))\n",
    "            all_labels_ori.update(labels_comp)\n",
    "            print('all_labels_ori: %s' % (all_labels_ori))\n",
    "            num_of_each_label = dict(Counter(labels_comp))\n",
    "            print('num_of_each_label: %s' % (num_of_each_label))\n",
    "            all_num_of_each_label.append(num_of_each_label)\n",
    "            print('all_num_of_each_label: %s' % (all_num_of_each_label))\n",
    "                    \n",
    "        all_num_of_labels_occured += len(all_labels_ori)\n",
    "        print('\\n all_num_of_labels_occured: %s' % (all_num_of_labels_occured))\n",
    "        \n",
    "        # calculate subtree kernel with h iterations and add it to the final kernel\n",
    "        print('\\n --- calculating kernel matrix ---')\n",
    "        for i in range(0, len(Gn)):\n",
    "            for j in range(i, len(Gn)):\n",
    "                labels = set(list(all_num_of_each_label[i].keys()) + list(all_num_of_each_label[j].keys()))\n",
    "                print('\\n labels: %s' % (labels))\n",
    "                vector1 = np.matrix([ (all_num_of_each_label[i][label] if (label in all_num_of_each_label[i].keys()) else 0) for label in labels ])\n",
    "                vector2 = np.matrix([ (all_num_of_each_label[j][label] if (label in all_num_of_each_label[j].keys()) else 0) for label in labels ])\n",
    "                print('vector1: %s' % (vector1))\n",
    "                print('vector2: %s' % (vector2))\n",
    "                Kmatrix[i][j] += np.dot(vector1, vector2.transpose())\n",
    "                Kmatrix[j][i] = Kmatrix[i][j]\n",
    "                    \n",
    "        print('\\n Kmatrix: %s' % (Kmatrix))\n",
    "\n",
    "    return Kmatrix\n",
    "\n",
    "    \n",
    "# main\n",
    "import sys\n",
    "from collections import Counter\n",
    "import networkx as nx\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.graphfiles import loadDataset\n",
    "from pygraph.kernels.spkernel import spkernel\n",
    "\n",
    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
    "G1 = dataset[15]\n",
    "print(nx.get_node_attributes(G1, 'label'))\n",
    "G2 = dataset[80]\n",
    "print(nx.get_node_attributes(G2, 'label'))\n",
    "\n",
    "weisfeilerlehmankernel(G1, G2, height = 2)\n",
    "# Kmatrix = weisfeilerlehmankernel(G1, G2)"
   ]
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